Machine Learning Engineer / Data Scientist LLM Agents (London)
Role Description
We 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 plus
Key Responsibilities
Design 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 & Biases and internal dashboards
Collaborate with scientists and engineers to turn research ideas into working products
Required Qualifications
BSc, 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 Skills
Basic 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 Attributes
Team 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
- Company
- Springer Nature
- Location
- London, UK
- Employment Type
- Full-time
- Posted
- Company
- Springer Nature
- Location
- London, UK
- Employment Type
- Full-time
- Posted