# Senior / Data Scientist (Model Training and Evaluation) , Giant Robots Labs

**Company**: Giant Robots Labs
**Location**: Singapore
**Experience**: senior
**Job type**: full-time
**Category**: Data Science
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/govtech/jobs/4008638201?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_9c8136ea-39c

## Description

The Government Technology Agency (GovTech) is building an in-house capability to evaluate, select and adapt AI models for government use. As a Data Scientist on this team, you will work across the full model engineering lifecycle, from evaluation and diagnosis through data preparation, training and validation.

Responsibilities:

- Design and build evaluation suites to compare models on government-relevant tasks, behaviours and costs, and establish baselines across prompting, retrieval and tool use

- Diagnose model weaknesses through observational and controlled evaluations, and determine which gaps persist after prompting, retrieval and tool-use improvements

- Set up and operate model hosting and evaluation infrastructure, and measure inference performance and cost under realistic workloads

- Prepare authorised training data: curate demonstrations and preference data, generate and filter synthetic examples, implement quality checks, and maintain strict separation between training and evaluation data

- Run post-training experiments such as supervised fine-tuning, parameter-efficient tuning, preference optimisation and distillation, varying data composition and hyperparameters in a controlled way

- Evaluate trained models against untrained baselines, quantify improvements, regressions and costs, and document training recipes so that results can be independently reproduced

- Work with government agencies to assess data readiness, identify usable data sources, and assemble training examples and evaluation cases

- Engage external partners, including research institutes and local companies, for technical exchange and knowledge transfer

- Write clear technical documentation: evaluation reports, training specifications, cost analyses, and records of both successful and unsuccessful approaches

Requirements:

- Bachelor's degree or higher in Computer Science, AI/Machine Learning, Engineering, Mathematics, Statistics or a related field, with at least 2-3 years of relevant professional experience

- Strong programming proficiency in Python and hands-on experience with the modern deep learning stack (e.g. PyTorch, Hugging Face Transformers)

- Practical experience fine-tuning or post-training language models (e.g. supervised fine-tuning, LoRA/QLoRA, DPO or similar preference optimisation methods), with the ability to debug training runs and reason about failures

- Strong grounding in evaluation methodology and experiment design: constructing baselines and held-out test sets, controlling variables, and drawing honest conclusions from results, including negative ones

- Experience building and cleaning datasets for model training and evaluation, including data pipelines, deduplication, filtering and data quality assessment, and the patience to do this work well

- Working knowledge of GPU infrastructure and inference serving (e.g. vLLM, SGLang or similar), including quantisation and the measurement of throughput and cost, in cloud or on-premises environments

- Effective and critical use of LLMs and AI coding tools: able to leverage them to move fast, while exercising judgment on output quality

- Strong communication skills, with the ability to explain technical trade-offs and evaluation findings clearly to both technical and non-technical stakeholders

- Comfort with ambiguity and a disposition towards evidence: willing to report what the results show, including when an approach does not work

Preferred Requirements:

- Experience fine-tuning models that span multiple GPUs (e.g. with FSDP or DeepSpeed)

- Experience with reinforcement learning based post-training (e.g. RLHF, PPO, GRPO) or model distillation

- Experience with continued pre-training or large-scale pre-training of language models, including building and curating large training corpora

- Experience building evaluation harnesses or benchmarks for language models, including LLM-as-judge pipelines

- Experience with synthetic data generation for model training

- Experience handling sensitive data or working in regulated environments, including data governance and data handling requirements

- Experience engaging stakeholders across organisations, such as sourcing data from partner agencies or coordinating with external research partners

## Skills

### Required
- Python
- PyTorch
- Hugging Face Transformers
- deep learning
- evaluation methodology
- experiment design
- dataset building
- GPU infrastructure
- inference serving
- LLMs
- AI coding tools

### Nice to have
- FSDP
- DeepSpeed
- reinforcement learning
- model distillation
- continued pre-training
- large-scale pre-training
- synthetic data generation
- evaluation harnesses
- benchmarks
- LLM-as-judge pipelines

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