Total 3 job vacancies by Klass Engineering & Solutions Pte. Ltd.

Summary

We are seeking an AI/ML Researcher to lead the research and development of large language models (LLMs) solutions. The ideal candidate will have a minimum of 2 years of experience in NLP research and a deep understanding of core LLM components, including LLM modeling, information retrieval, entity-relation extraction, agentic frameworks, and reasoning. The role involves leveraging AI/ML algorithm framework, such as TensorFlow and PyTorch, to develop cutting-edge generative LLM solutions. Responsibilities include designing and conducting scientific experiments, utilizing NLP techniques and LLM core components to develop novel LLM solutions in cloud or on-premises environments. The candidate will also be expected to collaborate with the team, participate in code reviews, architecture discussions, and create comprehensive documentation. The ideal candidate should possess a strong foundation in statistical analysis, machine learning, deep learning, generative AI, and text representation techniques. Additionally, the candidate should have excellent communication skills, a strong work ethic, and a passion for delivering high-quality, innovative solutions.

Responsibilities:

• Leading in designing the research and experiment methodology, specifically in LLM core components.
• Conduct independent research and report experiment results, research findings to stakeholders.
• Collaborate with engineering team in various data engineering tasks, including data collection, preprocessing, quality control, and augmentation, to curate high-quality datasets essential for model training,
• Utilize analytical techniques to extract meaningful insights from large datasets
• Communicating regularly with supervisor and Research leads on scientifical progress, insight and research direction.
• Publishing research papers in reputable journals and conferences is valued, it is not a strict requirement for this role.
• Contribute to other program-related projects.
• Collaborate with engineering team to translate researched technology (low TRL) to engineering (high TRL).
• Candidates applying for senior staff positions are expected to mentor and support team members (or cross-team members) in technical advising.

Requirement:

• Masters or PhD in Computer Science, Computer Engineering, Statistics, Engineering, or related field.
• 2-4 years of experience in research and development, specifically in one or more of the following areas:

1. Natural language processing, specifically in large language model (LLM) core component, e.g., joint entity relation, information retrieval, prompt engineering, reasoning, sentiment analysis, vulnerabilities in LLM, etc.
2. Generative AI, specifically in generating synthetic data.

• Demonstrate an analytical mindset with adept problem-solving skills.
• Keep abreast of the latest advancements in program-related techniques and methodologies,
• Exhibit a strong drive to deliver optimal solutions within tight timelines.
• Strong communication and organizational skills.
• Comprehensive documentation of technical specifications, architectural designs, and best practices.

Summary

This is an applied AI research role. The researcher will design and build non-deterministic analytical tools — grounded in statistical modelling, predictive models, and machine learning — that work across structured and unstructured data. Another part of the role is developing AI agents powered by large language models (LLMs) that can reason over and orchestrate these tools to solve real analytical problems end to end. The researcher is expected to be rigorous with data, curious about intelligent systems, and motivated by seeing their work produce something that actually runs.

Responsibilities

Research & Analysis

• Work with structured datasets to identify patterns, trends, and statistically significant findings that inform research questions and business decisions.
• Apply and adapt predictive models (regression, classification, time series forecasting) and mathematical models to address defined research problems.
• Conduct exploratory data analysis (EDA) to assess data quality, distributions, and feature relevance prior to modelling.
• Design and run statistical experiments, including hypothesis testing, power analysis, and significance testing.
• Validate model performance using appropriate evaluation frameworks and communicate limitations honestly.

AI development

• Study and apply LLM-based agent frameworks to automate and orchestrate research workflows.
• Design and build AI agents that use LLMs as a reasoning engine, with data science models and pipelines exposed as callable tools.
• Integrate agent outputs with structured data — enabling agents to query databases, invoke predictive models, and interpret results.
• Evaluate agent performance: task completion, reasoning coherence, tool-use accuracy, hallucination rate, and failure handling.
• Stay current with the evolving agent landscape and share learnings with the team.

Technical development

• Write clean, well-documented Python code for data processing, feature engineering, model development, and result visualisation.
• Query and manipulate structured data from relational databases using SQL.
• Use core data science libraries including Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, etc.
• Contribute to shared codebases, following team coding standards and version control practices (KLASS Gitlab).
• Assist in building reproducible data pipelines and experiment tracking workflows.

Collaboration & communication

• Work closely with senior researchers and principal researchers to scope, plan, and deliver project workstreams.
• Contribute to the quarterly and annual research reports, translating technical findings into clear, accessible language.
• Present findings to internal and client-side collaborators, including non-technical audiences.
• Participate in team research reviews, peer code reviews, and knowledge-sharing sessions.

Requirements

Education

• Master’s or PhD – Computer Science, Mathematics, Statistics, Physics, Engineering, Data Science, or a closely related quantitative discipline. (Preferred)
• Bachelor’s Degree – In a relevant quantitative field, with demonstrable hands-on experience in data science (e.g. internships, research projects, competitions, or open-source contributions).
• Fresh Graduates – Encouraged to apply. We value strong fundamentals and intellectual curiosity over years of experience.

Technical skills — essential

• Python programming: proficiency with core data science libraries (Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, Seaborn).
• Data wrangling: handling missing data, outlier detection, feature engineering, and normalisation on structured datasets.
• Mathematical foundations: linear algebra, calculus, and optimisation as applied to machine learning and statistical modelling.
• Working knowledge of modelling families: supervised, unsupervised, and generative models — see the dedicated modelling section below for full detail.

Personal Qualities

1. Research mindsetIntellectually curious across both quantitative and AI domainsRigorous and detail-oriented in analysis and documentationComfortable with ambiguity and iterative problem-solvingHonest about uncertainty — in models and in LLM outputs
2. Working styleClear communicator, written and verbalCollaborative team player in a research environmentSelf-directed with good time managementEager to learn and receive constructive feedback

Experience

0–2 years of professional or research experience

We define experience broadly. Relevant experience includes academic research projects, dissertations with a data science component, internships, industry placements, personal or open-source projects, Kaggle competitions, or research assistant roles. We are more interested in the quality of your analytical thinking than the number of years on your CV.

Responsibilities

• Collaborate with cyber threat experts to enhance system security
• Conduct fuzzing, reverse engineering and code audit to look out for security flaws on software
• Develop proof-of-concept in software security flaws
• Investigate security flaws found and provide remedies to counter cyber threats

Required skills

• Diploma/Degree in Information System, Computer Science or Computer Engineering or equivalent
• Singaporean only
• Minimum 3 - 5 years experience

Preferred Skills

• Experience in reverse engineering
• Experience with code auditing and a deep knowledge of the different vulnerability types
• Experience in vulnerability finding
• Experience in C and python programming, ARM/AArch64assembly