Foundation Model Engineer

Overview

In this role you will advance foundation models across the full AI lifecycle, from experimentation to scalable deployment. You will work within a mission-driven team focused on safe, responsible AI at scale. You’ll design end-to-end training pipelines, optimize performance, and build robust evaluation and data workflows. You’ll collaborate with researchers and engineers to push technical depth and rigorous validation. This is an early-stage opportunity to shape how cutting-edge models are trained and deployed.

Pay / Benefits

• Competitive salary
• Benefits

Responsibilities

• Design and implement end-to-end LLM training pipelines
• Source and 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

Key requirements

• Proven experience training and fine-tuning LLMs or multimodal models (not just consuming APIs)
• Understanding of model evaluation/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
• Ability to debug and improve model performance systematically

• Python
• PyTorch or TensorFlow
• ML pipelines in production
• GPU usage and optimisation
• Model evaluation and validation

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