Role DescriptionWe are looking for a machine learning engineer with strong data science expertise to join the team working on large language models for life and natural science problems. Work involves building agentic workflows where LLMs reason, plan and act, as well as developing pipelines to train and fine-tune models. LangGraph is our main framework for agent development; knowledge of other agent stacks is a plusKey ResponsibilitiesDesign and build multi-step LLM agents with LangGraph and similar frameworks

Create data and ML pipelines for continual pre-training, supervised fine-tuning and RL alignment

Deploy models and retrieval services on containerised infrastructure with reliable CI/CD

Monitor and improve agent performance with Weights and Biases and internal dashboards

Collaborate with scientists and engineers to turn research ideas into working products

Required QualificationsBSc, MSc or PhD in Computer Science, Data Science or a related field

Strong Python skills with PyTorch, HuggingFace Transformers and Datasets

Proven track record fine-tuning and serving large language models in real-world settings

Hands-on experience building pipelines with reinforcement-learning algorithms such as PPO and GRPO

Competence with containers, automated testing and software-engineering best practice

Useful SkillsBasic experience with GCP and infrastructure-as-code workflows

Hands-on experience using vector, graph and relational databases, plus SQL and data modelling

Experience with multimodal models and emerging agent protocols such as MCP and A2A

Ability to implement model safety and guard-rail measures

Personal AttributesTeam player with clear communication

Analytical and detail-oriented problem solver

Curious and quick to learn new methods

Comfortable in a fast-moving research environment

Committed to delivering maintainable, reliable software

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