Foundation Model Engineer

Foundation Model Engineer Cambridge | Competitive salary + benefits About the Company Our client is a well-funded, mission-driven organisation working at the forefront of AI development. Led by an experienced team of founders, investors and engineers, they are focused on building safe, responsible AI systems and are growing quickly from a Cambridge base. The Opportunity Our client is investing heavily in their machine learning infrastructure and compute capability to accelerate model development and inference. This is a chance to join at an early stage and work across the full AI lifecycle, from experimentation through to scalable deployment, with a strong emphasis on technical depth and rigour. They're looking for a highly skilled Foundation Model Engineer with hands-on experience building, training, evaluating and deploying LLMs or multimodal models end to end. The role will focus primarily on model development, data pipelines and system performance. What You'll Do

• Design and implement end-to-end LLM training pipelines
• Source and, where appropriate, preprocess datasets for training and evaluation
• Fine-tune and optimise open weight models (LLMs, vision or traditional ML)
• Build evaluation frameworks and define performance metrics
• Develop and maintain data pipelines and training workflows
• Analyse training pipelines and optimise for latency, cost and scalability
• Implement monitoring, logging and feedback loops for continuous improvement
• Experiment with modern AI tooling and services to assess how they can be leveraged

What You'll Bring

• Proven experience training and fine-tuning LLMs or multimodal models (not just consuming APIs)
• A solid understanding of model evaluation and validation, overfitting and bias/variance trade-offs, and data quality and feature engineering
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building and maintaining ML pipelines in production
• Familiarity with GPU usage and optimisation
• The ability to debug and improve model performance systematically

Also Valued

• Knowledge of distributed training or large-scale data processing
• Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning)
• A background in applied research or publishing