Total 45 infrastructure engineer job vacancies in Education / Training category

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Sustainable-Composite-Materials-for-Coastal-Infrastructure%29/33280-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

The National University of Singapore is seeking a motivated Research Fellow to join an applied research project on bio-based Fiber Reinforced Polymer (FRP) composites for low-carbon corrosion protection of steel structures in severe marine environments.

The project aims to develop and validate sustainable FRP systems derived from bio-based resins as a next-generation solution for protecting coastal and marine infrastructure. The Research Fellow will work closely with a multidisciplinary team from NUS and industry partner Fyfe Asia Pte Ltd, contributing to laboratory testing, durability assessment, field exposure studies, and life-cycle evaluation of bio-based FRP technologies.

The successful candidate will have the opportunity to work on a high-impact national research project aligned with Singapore’s coastal protection, infrastructure resilience, and sustainability goals. This role offers strong exposure to industry collaboration, applied materials research, marine durability testing, and technology translation.

Key responsibilities include:

•    Conduct material characterization of bio-based FRP composites, including mechanical and physical property testing.
•    Design and perform durability tests under simulated marine conditions, including salt spray, water resistance, UV exposure, chemical resistance, accelerated aging, and long-term exposure studies.
•    Evaluate the corrosion protection performance of FRP-wrapped steel and compare it with conventional protection methods such as coatings and galvanizing.
•    Support field investigations and exposure testing at coastal or marine test sites.
•    Analyze experimental data and develop service-life insights for FRP-protected steel structures.
•    Assist in life-cycle cost and carbon emission assessments comparing bio-based FRP with conventional FRP and traditional corrosion protection technologies.
•    Prepare technical reports, research publications, presentations, and project deliverables.
•    Collaborate with academic researchers, industry partners, and relevant stakeholders to support technology validation and translation.

Why Join Us?
This is an opportunity to contribute to a timely and impactful research project addressing one of the most pressing challenges for coastal cities: protecting critical infrastructure in harsh marine environments while reducing carbon emissions. You will work with a multidisciplinary academic-industry team, gain hands-on experience in advanced FRP materials and marine durability research, and help translate sustainable materials technology into real-world coastal protection applications in Singapore and beyond.

Job Requirements

•    Possess a PhD in Civil Engineering, Materials Science and Engineering, Mechanical Engineering, or a closely related discipline.
•    Strong background in one or more of the following areas:
     - FRP composites or polymer-based materials/
     - Corrosion protection of steel structures/
     - Marine durability of construction materials/
     - Sustainable construction materials/
     - Structural materials testing/
•    Familiarity with relevant standards such as ASTM, ISO, ACI, ABS, or equivalent testing and design guidelines would be advantageous.
•    Ability to conduct independent research, analyze data rigorously, and prepare high-quality technical reports and publications.
•    Good communication skills (Written and spoken) and the ability to work effectively in a multidisciplinary team involving university researchers and industry partners.
•    Open to fixed-term contract. 

Key Responsibilities

• Develop data-driven energy disaggregation models to estimate machine-, process-, and batch-level energy consumption from aggregated or limited metering data.
• Design and implement energy monitoring, analytics and benchmarking algorithms for energy performance tracking, anomaly detection, and causal analytics related to energy-saving of industrial equipment and manufacturing processes.
• Conduct experiments, model validation, and performance evaluation using industrial datasets.
• Work closely with project partners, engineers, and researchers to translate research outcomes into deployable industrial solutions.

Requirements

• Bachelor’s or Master’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, Data Science, Industrial Engineering, or a related field.
• Strong programming skills in Python, with experience in data analytics, machine learning, and time-series processing.
• Knowledge of energy systems, power consumption analysis, industrial equipment, or manufacturing processes.
• Experience with machine learning techniques for regression, classification, anomaly detection, forecasting, or signal disaggregation.
• Ability to process and analyze large-scale time-series datasets.
• Good problem-solving skills and ability to work independently in applied research and development projects.
• Good written and verbal communication skills.

Preferred Qualifications

• Experience in non-intrusive load monitoring, energy disaggregation, virtual sensing, or industrial energy analytics.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience with causal analytics.
• Knowledge of manufacturing systems, additive manufacturing, CNC, heat treatment, pumps, compressors, motors, or other industrial equipment.
• Prior experience in applied R&D projects with industry partners is an advantage.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.

Interested applicants are invited to apply directly at theNUS Career Portal. Please note your application will only be processed if youapply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Engineer-%28Robotics%29/33299-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

•             Robot design.

The candidate is expected to assist in the design, fabrication, and testing of a novel robotic system based on application-related specifications.

•             Robot experiments.

The candidate is expected to support the definition of experimental protocols, in terms of trials and parameters to measure, to help with the experimental set-up, experimental validation, performance testing, and iterative improvements of developed prototypes and with the recording and analysis of experimental results.

•             Collaborate with cross-functional teams to integrate mechanical, electrical, and software components into functional robotic systems.

•             Support applied projects in collaboration with industrial partners and contribute to technology transfer efforts.

•             Present research findings at international workshops, conferences, and internal project meetings.

•             Contribute to the preparation of technical reports and assist in writing articles for publication in top-tier journals and conferences.

Qualifications

•             Mechanical Engineering degree

•             Robotics expertise: knowledge of robotics and soft robotics fundamentals, skills for robot design.

•             Research experience, even as a student, including skills for experimental research and paper preparation.

•             Hands-on experience in robotics, soft robotics, or interactive systems.

•             Proficiency in CAD software, prototyping, and mechatronics.

•             Ability to work collaboratively in a multidisciplinary team environment.

•             Strong problem-solving skills and creativity.

Job Title: AI Research Engineer - Vision-Language Action Models

Griffin Labs is looking for a Founding AI Research Engineer to lead the adaptation and deployment of Vision-Language-Action (VLA) models. We are focused on solving high-dexterity manipulation through cross-embodiment learning—training models that generalize across diverse robotic platforms and hardware configurations.

The Role

You will own the full development lifecycle of autonomous capabilities. This is an end-to- end mission: from reviewing raw trajectory data and designing model architectures to deployment and fleet-level monitoring. You aren’t just training models; you are building the systems that allow them to function in the real world.

Key Responsibilities:

• End-to-End Ownership: Lead the "data-to-deployment" loop—curating datasets, fine-tuning VLA models, and building the monitoring systems that ensure fleet-level reliability.

• Cross-Embodiment Learning: Develop and optimize models capable of handling diverse robot morphologies and kinematic constraints.

• Model Adaptation: Architect and fine-tune VLA stacks that bridge high-level reasoning with low-level whole-body control.
• Academic Collaboration: Work alongside expert research advisors from institutions like Stanford, NTU, and SUTD to push the boundaries of what's possible in robotics.

• Validation: Design robust evaluation metrics in Isaac Sim/MuJoCo and bridge the Sim2Real gap for a production fleet.

Requirements

• Technical Depth: A strong fundamental understanding of Transformers, Diffusion architectures, and LLMs/VLMs, particularly as they apply to sequential decision-making or action prediction.

• ML Expertise: Deep proficiency in PyTorch and experience fine-tuning large-scale multimodal models.

• Cross-Embodiment: A strong interest or background in generalizing AI across different robot types.

• Simulation: Fluency in environments like Isaac Sim or MuJoCo and the nuances of the "Reality Gap."

• Software Foundations: High proficiency in Python.

• Founding Mindset: High agency and the ability to build necessary tooling, scripts, and evaluation frameworks from scratch.

Why Griffin Labs?

We are a lean, technical team moving at the pace of frontier AI. As a founding hire, you will have a direct hand in shaping our technical North Star and defining how the next generation of robots learns from the world

Job Summary

About the Role

The Institute of High Performance Computing (IHPC), A*STAR, is seeking a highly motivated Research Engineer to develop and deploy advanced AI systems that translate cutting-edge research into scalable real-world applications.

The successful candidate will work closely with research scientists to implement, optimize, and operationalize AI models across healthcare and biomedical domains. This role combines software engineering, machine learning, and systems engineering to bridge the gap between research and deployment.

Key Responsibilities

AI System Development

• Design, develop, and maintain machine learning and deep learning systems.
• Implement state-of-the-art AI algorithms from research prototypes into production-quality software.
• Build scalable data processing and model training pipelines.
• Develop multimodal AI systems integrating text, image, video, sensor, and structured data.
• Optimize model training, inference, and deployment workflows.

Research Translation

• Collaborate with AI researchers to translate novel algorithms into robust software solutions.
• Reproduce and benchmark research models.
• Develop reusable AI frameworks, libraries, and tools.
• Support experimentation, model evaluation, and performance optimization.

Software & Infrastructure Engineering

• Design scalable cloud and on-premise AI infrastructure.
• Develop APIs, backend services, and model-serving platforms.
• Implement automated testing, monitoring, and CI/CD pipelines.
• Support large-scale distributed training and inference systems.

Deployment & Integration

• Deploy AI solutions into healthcare, scientific, and enterprise environments.
• Integrate AI systems with operational workflows and external platforms.
• Develop monitoring tools for model performance and reliability.
• Support technology transfer and production deployment activities.

Requirements

Essential

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or related disciplines.
• Strong programming skills in Python.
• Experience with PyTorch, TensorFlow, or similar machine learning frameworks.
• Experience in software engineering best practices.
• Familiarity with Linux, Git, and software development workflows.
• Strong problem-solving and system design skills.

Preferred

• Experience with:Large Language ModelsMultimodal AIComputer VisionMLOps platformsCloud Computing (AWS, Azure, GCP)Containerization (Docker, Kubernetes)Distributed ComputingGPU Computing

What We Offer

• Opportunity to work on cutting-edge AI technologies with real-world impact.
• Access to world-class computational infrastructure and research facilities.
• Collaboration with leading researchers, clinicians, scientists, and industry partners.
• A dynamic environment supporting innovation, learning, and career growth.

We are looking for a research engineer to customize and make our ground penetrating radar market ready. The role will focus on the hardware side of the project which involves the miniaturization of the GPR, antenna fabrication and integration, product improvement and customization for the market.

Key Responsibilities:

• Ground penetrating radar miniaturization, customization and integration

• Improve the product performance and make the product more appealing to the market

• Interact and coordinate with industry and customers to provide a service

• Present the product and explain the product well

• Help to raise funds for a spin-off and do marketing for the product.

• Other administrative duties as required.

Job Requirements:

• Bachelor qualification degree in Electrical and Electronic Engineering or related field

• At least some of relevant experiences in similar role / technical hands-on experience in hardware integration

• Familiarity with funding system in Singapore

• Good written and oral communication skills

• Proficiency in hardware integration, system programming

• Competent in hands-on work, presentation and fund raising.

• Interpersonal skill such as develop solutions under strict timelines, meticulous and eye for details / excellent organizational / time management skills.

• Entry level candidates are welcome to apply

We regret to inform that only shortlisted candidates will be notified.

About Fragment Works and Moment AI

FRAGMENT WORKS PTE. LTD. is a Singapore-incorporated technology company operating the Moment AI platform at https://momentvideo.ai/.

Moment AI is a leading VC-backed video AI foundation model company developing advanced video AI technologies and business-to-business infrastructure for enterprise customers. The company focuses on building scalable, production-grade video AI systems that support real-world commercial applications across video generation, video understanding, model deployment, and model-sales operations.

Through API-based capabilities, Moment AI serves companies in the short-video, creator economy, advertising, media, and AI application sectors. Its platform is designed to help enterprises integrate video AI into their products and workflows efficiently, enabling automated content creation, intelligent video analysis, and next-generation AI-powered media experiences.

Moment AI is founded by a team of video industry veterans with deep operational and technical experience in scaling short-video platforms to millions of users. The founding team brings together battle-tested entrepreneurs, infrastructure builders, and AI researchers with hands-on experience across video platforms, creator ecosystems, high-concurrency video infrastructure, and multimodal AI.

About the Role

We are building our own foundation model for video generation, based on DiT and Flow Matching architectures. We are looking for a Training Infrastructure Engineer who can turn cutting-edge research code into a stable, scalable, and high-throughput training system running on large-scale GPU clusters.

This role is ideal for an engineer who enjoys solving deep systems problems at the intersection of distributed training, CUDA performance, video data pipelines, model training stability, and large-scale ML infrastructure. You will work closely with researchers and platform engineers to ensure that our video generation training stack can reliably produce results at the thousand-GPU scale.

Key Responsibilities

• You will design, optimise, and maintain large-scale distributed training systems for video generation foundation models. This includes implementing and improving training strategies such as FSDP, tensor parallelism, context parallelism, and Ulysses-style sequence parallelism, with a strong focus on improving throughput, scaling efficiency, and MFU.
• You will build and optimise PB-scale video data pipelines, including NVDEC-based video decoding, VAE latent caching, variable-resolution bucket sampling, and efficient data loading for high-throughput model training.
• You will work on memory and performance optimisation across the training stack, including FlashAttention,FP8 mixed precision, Triton kernels, CUDA-aware profiling, activation check pointing strategies, and communication-computation overlap.
• You will also be responsible for training stability and reliability. This includes identifying the root causes of loss spikes, divergence, slow nodes, communication bottlenecks, check point failures, and data-related instability, as well as designing mechanisms for fast checkpoint recovery and automatic exclusion of problematic nodes.

Requirements

• The ideal candidate has strong hands-on experience with PyTorch distributed training and a solid understanding of CUDA architecture, GPU memory hierarchy, NCCL communication, and performance profiling.
• You should have source-level familiarity with at least one major large-scale training framework, such as Megatron-LM, DeepSpeed, PyTorch FSDP, or TorchTitan, and be comfortable reading, modifying, and debugging framework internals.
• You should have at least one year of practical experience training models on large GPU clusters of 256 GPUs or more, with proven experience in debugging distributed training failures and improving system-level training efficiency.
• Strong candidates will be able to reason across the full training stack, from data ingestion and model parallelism to kernel-level optimisation and fault-tolerant training operations.

Preferred Qualifications

• Experience with DiT, diffusion models, Flow Matching, or video generation models would be highly advantageous.
• Experience processing large-scale video datasets, building video decoding pipelines, or working with VAE latent caching systems would be a strong plus.
• Hands-on experience writing or optimising Triton, CUDA, or CUTLASS kernels would be valuable.
• Familiarity with open-source video generation projects such as HunyuanVideo, Wan, CogVideoX, or similar systems at source-code level would also be beneficial.

What We Offer

You will work with real large-scale compute resources, including access to thousand-GPU-level training infrastructure. You will join a team that treats large-scale model training as a rigorous engineering discipline, not just a research experiment.

We provide an environment where engineers can work on technically meaningful infrastructure problems, collaborate closely with frontier model researchers, and contribute to open-source or publication work where appropriate and within applicable compliance boundaries.

Employment Practices

We are committed to fair and merit-based hiring. All candidates will be assessed based on job-related skills, experience, and ability to perform the role. We welcome applications from qualified candidates and do not discriminate on the basis of age, race, gender, religion, marital status, family responsibilities, disability, or other non-job-related characteristics.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-EngineerAssistant-%28Interdisciplinary%29%2C-ChBE/33229-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

Positions are available for Research Engineers, Research Assistants, and Ph.D. students in the research group of Prof. Soh Siow Ling, at the Department of Chemical and Biomolecular Engineering, National University of Singapore (NUS).  

The group’s general program of research involves the coupling of organic materials, mechanical forces, and electrostatics at different interfaces (e.g., solid, liquid, or gas). This coupling is currently greatly under-explored and poorly understood in science. At the same time, the coupling gives rise to many fascinating new natural phenomena for exciting fundamental discoveries and development of impactful and original technologies. 

The general approach of the group is to conduct original programs and new directions of research with strong fundamental science. The group is highly interdisciplinary and involves engineering (e.g., fluid mechanics), materials science, chemistry, and physics. Different organic materials are used, including polymers (e.g., stimuli-responsive polymers), hydrogels, and covalent organic frameworks (COFs). 

One project involves an original system that harvests water energy from nature (e.g., rain and rivers) and directly generates electricity by solid-liquid charge separation with high energy conversion efficiency. Other projects involve clearing virus-laden atmospheric aerosols and preventing the spread of infectious diseases (e.g., COVID-19) and development of clean surfaces that do not adhere any dust on them. Another research involves stimuli-responsive polymers for small molecular separation, smart delivery, and intelligent soft machines.

A recent exciting and emerging field from our group is the development of novel mechanocatalysts for performing mechanochemical reactions with high energy conversion efficiency. Harvesting energy from mechanical forces of nature (e.g., wind or water flows) is a novel and sustainable approach to performing chemical reactions. 

Qualifications

The ideal candidate is preferably someone open-minded to conducting original fields of research, independent, and hardworking. Because the research of the group is highly interdisciplinary and many projects in different disciplines are ongoing, candidates with a Bachelor’s degree from a wide range of backgrounds will be considered for different projects, including engineering (e.g., chemical, mechanical, etc), materials science, chemistry, and physics.  

For more information on the background of the research, please refer to: https://blog.nus.edu.sg/chessl/

COMPANY DESCRIPTION

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

RESPONSIBILITIES

The candidate will be responsible for conducting design and development of sensor networks and Internet-of-Things (IOT) systems for in-home sensing and machine learning models for analyzing digital biomarkers and human activity data for the purpose of cognitive health monitoring and wellbeing analysis. Successful candidate will join an active research team led by Prof TAN Ah Hwee from School of Computing and Information Systems, Singapore ManagementUniversity.

Candidate's core responsibilities are:

• Project administration, user management
• Design, deployment and maintenance of sensor networks and Internet of Things (IOT) system for in-home sensing of human behaviour and activities
• Integration of in-home sensors, home gateway, and backend cloud servers for data collection
• Development of software for digital biomarker feature extraction and data analytics
• Building of machine learning and predictive models based on digital biomarkers and spatial temporal data
• Documentation and preparation of technical reports
• Other duties as assigned

• Diploma or Bachelor's degree in Computer Science, Information Technology, Information Systems or closely related disciplines from an nstitution of higher learning
• Minimum 1-2 years of relevant research experience with sensors, Internet-of Things, data analytics, artificial intelligence, or related fields
• Good analytical, technical and problem solving skills
• Knowledge/Proficiency in hardware sensors, cloud computing, networking and programming languages, such as Python, Java and C/C++• Competency in application prototype design and development
• Good writing, communication and interpersonal skills

#LI-JN2

COMPANY DESCRIPTION

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

RESPONSIBILITIES

• This position is for School of Computing and Information Systems (SCIS).
• Build and maintain end-to-end data pipelines, including data ingestion, cleaning, validation, feature engineering, and dataset versioning.
• Develop and manage research codebases for optimization and machine learning experiments, ensuring reproducibility and scalability.
• Implement optimization algorithms (e.g., LP/MIP, heuristics, decomposition methods, simulation-based approaches) and integrate them with learning components.
• Develop internal tools (e.g., APIs, evaluation frameworks, benchmarking pipelines, dashboards) to support research workflows.
• Support large-scale experimentation, including performance optimization, parallelization, and reproducibility.
• Collaborate closely with researchers to translate conceptual models into robust, reusable implementations
• Other duties as assigned.

• Bachelor's or Master's degree in Computer Science, Software Engineering, Industrial Engineering, Data Science, Operations Research, or a related field.
• Strong programming skills in Python; experience with production-quality code (testing, modular design, version control).
• Experience building data and/or machine learning pipelines (e.g., pandas, SQL, workflow tools).
• Experience implementing algorithms and running large-scale experiments.
• Familiarity with Linux environments, Git, and reproducible workflows (e.g., conda, Docker).
• Proficiency in using AI tools to support development and experimentation.
• Strong problem-solving, communication, and collaboration skills.
• Preference for candidate who is familiar with
  • Optimization tools (e.g., Gurobi, CPLEX, OR-Tools, Pyomo, cvxpy, JuMP)
  • Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experiment tracking tools (e.g., MLflow, Weights & Biases)
  • Experience with cloud or HPC environments and parallel computing frameworks (e.g., Ray, Dask, MPI)
  • Experience developing internal research tools (e.g., benchmarking systems, evaluation pipelines, dashboards).

#LI-JN2

Job Summary

Job Purpose

The Knowledge Engineer will be responsible for designing, constructing, and maintaining knowledge graphs derived from heterogeneous enterprise documents. The role involves leveraging Large Language Models (LLMs) for knowledge extraction, ontology-driven structuring, and question answering over knowledge graphs to support intelligent search, reasoning, and decision-making systems.

Key Responsibilities

• Leverage LLMs to extract structured knowledge (entities, relations, attributes) from unstructured and semi-structured documents such as:Maintenance manualsProduct design documentsTroubleshooting guidesLessons learned reports
• Process documents in multiple formats, including Word, PDF, PowerPoint, and Excel.
• Design, construct, and maintain knowledge graphs, including:Schema and ontology designEntity and relationship modellingData normalisation and validation
• Implement and manage knowledge graph storage using graph databases such as Neo4j and GraphDB.
• Develop LLM-powered question answering and interaction pipelines over knowledge graphs, enabling:Natural language queriesContext-aware and explainable responses grounded in structured knowledge
• Collaborate with domain experts and stakeholders to refine ontologies, extraction logic, and use cases.

Required Skills & Experience

• Strong understanding of ontology design, knowledge graph modeling, and semantic data representation.
• Proficiency in Python, with experience building data processing and NLP pipelines.
• Solid understanding of prompt engineering and LLM-based workflows.
• Hands-on experience working with LLMs, including both:Closed-source models (e.g., GPT-based APIs)Open-source models (e.g., LLaMA, Qwen, etc.)
• Experience with graph databases, particularly Neo4j and/or GraphDB.

Preferred / Advantageous Skills

• Experience in information extraction, document understanding, or NLP pipelines.
• Familiarity with RDF, OWL, SPARQL, or Cypher is a plus.
• Experience integrating LLMs with structured knowledge sources (e.g., KG-augmented QA, RAG with graphs).

Key Competencies & Attributes

• Strong analytical and problem-solving skills, with the ability to translate unstructured information into well-defined data models.
• Ability to work independently while collaborating effectively within a multidisciplinary team.
• Good communication skills to engage with technical and non-technical stakeholders.
• Demonstrated willingness to continuously learn and keep up with advances in LLMs, NLP, and knowledge engineering.

Job Summary

The Institute of High Performance Computing (IHPC), A*STAR, was established in April 1998 to provide leadership in high performance computing as a strategic resource for scientific inquiry and industry development. Our mission is to advance science and technology, and develop leading-edge applications through high performance computing and computational science. We have long-term ties with industry partners, including multinational companies and local SMEs, which ensures that our R&D activities stay focused and relevant to real-world needs. We also have active cooperation with the world’s top academics and academic institutions.

We are looking for a motivated and versatile individual to join the Healthcare team. The role will support the development, adoption, and continuous improvement of reusable AI assets and platforms through ecosystem engagement, platform operations, product thinking and practical UI/UX capability.

Research Engineer (Reusable Healthcare AI Assets)

The successful candidate will work closely with researchers, engineers, product owners, healthcare and public-sector stakeholders, and industry partners to identify user needs, translate them into actionable platform requirements, and drive the adoption of reusable AI assets. The role is suitable for someone who can engage stakeholders effectively, structure feedback into product and operational priorities, and contribute to simple, intuitive user journeys, wireframes, prototypes, dashboards, and documentation for AI platforms.

Requirements

• Experience in at least one of the following areas: AI/ML platform delivery, product or platform operations, technical project management, ecosystem or stakeholder engagement, developer relations, digital transformation, UI/UX design, or technology adoption.
• Working understanding of AI/ML systems, data-driven applications, software platforms, APIs, cloud environments, MLOps, or reusable software/assets. Deep research specialisation is not required, but the candidate must be comfortable working with technical teams.
• Ability to translate between domain needs, user pain points, operational constraints, and technical implementation requirements.
• Practical exposure to user research, journey mapping, wireframing, prototyping, usability testing, information architecture, or interface review.
• Strong written and verbal communication skills, with the ability to explain technical capabilities and trade-offs to both technical and non-technical audiences.
• Good planning, coordination, and stakeholder management skills, with the ability to manage multiple workstreams independently in a fast-moving environment.

Preferred qualifications (one or more of the following)

• Experience in healthcare, biomedical, public-sector, or regulated digital systems.
• Hands-on experience with UI/UX or prototyping tools such as Figma, Miro, FigJam, Balsamiq, or equivalent.
• Ability to build lightweight prototypes, demos, or dashboards using tools such as React, JavaScript/TypeScript, HTML/CSS, Streamlit, Gradio, Dash, Tableau, Power BI, or similar.
• Experience managing reusable asset catalogues, platform onboarding journeys, technical documentation portals, design systems, knowledge bases, or developer-facing resources.
• Familiarity with agile delivery and collaboration tools such as Jira, Confluence, Git, GitHub/GitLab, or equivalent.
• Awareness of responsible AI, model evaluation, AI safety, data privacy, cybersecurity, and governance considerations for AI systems.

Interested applicants should submit their curriculum vitae, together with a short cover note describing relevant experience in ecosystem engagement, platform operations, AI/ML systems, and UI/UX or product-related work. The level of appointment will be commensurate with experience.

For applicants, please send your curriculum vitae to Dr Ricardo Shirota Filho (ricardo_shirota_filho@a-star.edu.sg)

Role Overview

As a University of Applied Learning, SIT works closely with industry in its research pursuits. Our research staff have the opportunity to develop industry-relevant applied research capabilities while contributing to impactful projects that address real-world operational needs.

We are seeking a Research Engineer to join an applied research project focused on the development of an embodied AI robotic system for urban infrastructure inspection and maintenance. The project involves the design, integration, and validation of robotic mechanisms, sensing systems, and automated workflows for outdoor infrastructure maintenance applications.The successful candidate will work closely with a multidisciplinary research team and industry partners to investigate, prototype, test, and implement practical robotic solutions that can operate in real-world environments and support more efficient, safe, and scalable infrastructure maintenance.

Key Responsibilities

• Design, develop, and integrate customised mechanical mechanisms for robotic systems, including robotic end-effectors, grippers, tool-changing interfaces, material handling systems, and task-specific automation modules.
• Develop 3D CAD models, fabrication drawings, Bills of Materials (BOMs), and technical documentation using SolidWorks.
• Perform mechanical design analysis and engineering calculations, including load estimation, actuator sizing, structural integrity, tolerance analysis, and vibration/shock mitigation.
• Ensure mechanical designs are compatible with robotic arms, mobile platforms, sensors, and mechatronic systems, considering payload, reachability, safety, manufacturability, and maintainability.
• Build, assemble, and commission mechanical prototypes and experimental robotic systems.
• Conduct laboratory and field testing, analyse results, troubleshoot issues, and implement design improvements through iterative development.
• Source and evaluate commercial components, and work with vendors, fabricators, and contractors to support fabrication and assembly.
• Support field deployments and technology demonstrations with industry partners.
• Prepare technical reports, design documentation, test reports, risk assessments, and project deliverables.
• To liaise and communicate with internal and external stakeholders to ensure project deliverables are met.
• Any other ad-hoc duties assigned by Supervisor.

Requirements

• Bachelor’s degree or higher in Mechanical Engineering, Mechatronics Engineering, Robotics Engineering, or a related discipline.
• Strong hands-on experience in mechanical design, prototyping, fabrication, and system integration.
• Proficiency in SolidWorks for 3D modelling, assembly design, and engineering documentation.
• Good understanding of mechanical design principles, materials, manufacturing processes, and engineering analysis.
• Experience working with robotic systems, sensors, actuators, or mechatronic systems is advantageous.
• Strong problem-solving skills, attention to detail, and ability to work independently and within multidisciplinary teams.
• Willingness to conduct laboratory and field testing when required.

About the Role 

The Advanced Remanufacturing and Technology Centre (ARTC) is looking for a Lead / Senior Solution Architect to drive the design and implementation of enterprise-scale agentic AI systems for industrial and enterprise applications. This role focuses on architecting AI-enabled platforms and workflows that leverage LLMs, tool orchestration, autonomous agents, enterprise integrations, and scalable cloud-native systems to support intelligent operations and decision-making. You will work closely with engineering teams, AI researchers, and business stakeholders to shape technical strategy, define system architectures, and lead the delivery of production-grade AI solutions. We are looking for experienced architects who combine strong software engineering and system design capabilities with deep interest in emerging agentic AI technologies and practical enterprise adoption. 

Key Responsibilities

• Architect and lead development of enterprise-scale agentic AI systems and platforms 
• Design AI orchestration workflows involving LLMs, tools, agents, and enterprise integrations 
• Define scalable, secure, and reliable system architectures and engineering practices 
• Collaborate with stakeholders to translate operational needs into AI-enabled solutions 
• Evaluate and adopt emerging technologies related to agentic AI and enterprise AI systems 
• Provide technical leadership and mentorship to engineering teams 

Job Requirements

• Degree in Computer Science, Software Engineering, or related fields 
• Strong experience in software architecture, backend systems, or enterprise platform development 
• Experience designing scalable cloud-native or distributed systems 
• Familiarity with LLMs, AI application architectures, and orchestration frameworks 
• Interest or experience in agentic AI systems, automation workflows, or multi-agent applications 
• Strong communication, problem-solving, and stakeholder management skills 

Good to have 

• Experience with cloud platforms such as Azure, AWS, or GCP 
• Familiarity with Docker, Kubernetes, CI/CD pipelines, or MLOps practices 
• Experience building AI-enabled enterprise applications, copilots, or intelligent automation solutions 
• Exposure to AI governance, observability, or security considerations for AI systems 
• Experience working with manufacturing, industrial systems, or enterprise digital transformation initiatives 

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.

Responsibilities:

• Design and develop mechanical components, tooling, fixtures, and experimental setups to support welding, joining, and additive manufacturing processes.
• Create 3D CAD models and detailed engineering drawings for mechanical assemblies, test rigs, and manufacturing equipment.
• Support the design, fabrication, and assembly of prototype systems and hardware modules for research and development projects.
• Evaluate and select appropriate materials, manufacturing methods, and fabrication processes for designed components.
• Conduct mechanical analysis and validation of designs, including structural, thermal, or vibration considerations where required.
• Support prototype build, testing, and troubleshooting of mechanical systems and experimental platforms.
• Collaborate with internal teams, suppliers, and manufacturing partners for component fabrication, procurement, and system integration.
• Contribute to the development and improvement of mechanical hardware and equipment used in robotic welding, joining, and additive manufacturing systems.
• Support the group's process development activities, including experimental trials, setup of test configurations, data collection, and evaluation of process performance.
• Participate in concept development, feasibility studies, and technology development projects related to advanced manufacturing processes.
• Prepare and maintain technical documentation, including design reports, engineering drawings, test procedures, and project documentation.
• Support equipment setup, maintenance, and continuous improvement of laboratory and manufacturing systems.
• Ensure compliance with safety procedures, risk assessments, and laboratory best practices.
• Support other technical activities within the team, group, or organisation as required.

Job Requirements

Minimum Requirements (Must-have)

• Bachelor's degree in Mechanical Engineering, Manufacturing Engineering, Materials Engineering, or a related discipline.
• Strong foundation in mechanical design and engineering fundamentals, including structural mechanics, material behavior, and manufacturing processes.
• Experience with mechanical CAD and 3D modelling tools (e.g., SolidWorks, Inventor, Creo, NX or equivalent) for design and engineering documentation.
• Experience in designing mechanical components, tooling, fixtures, or assemblies for manufacturing or experimental systems.
• Good understanding of manufacturing techniques and fabrication processes, such as CNC machining, metal fabrication, additive manufacturing, or prototyping methods.
• Experience preparing engineering drawings and technical documentation, including basic understanding of Geometric Dimensioning and Tolerancing (GD&T).
• Hands-on experience with prototyping, testing, and mechanical system troubleshooting.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work both independently and collaboratively in a multidisciplinary engineering environment.
• Strong written and verbal communication skills.
• Ability to work in a dynamic research and development environment and adapt to evolving project requirements.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.