Senior ML Ops Engineer

Salary: £70,000 - 110,000 per year

Requirements:

• Postgraduate degree in Machine Learning, Computer Science, Software Engineering or a related technical field; PhD preferred or MSc with equivalent industry experience
• Solid hands-on experience operating ML training and serving workflows in production environments
• Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train
• Experience operating experiment tracking systems and model registry systems such as MLflow, Weights and Biases or equivalent
• Solid understanding of cloud infrastructure for ML, including compute, storage and networking, sufficient to specify requirements clearly and diagnose infrastructure-related issues
• Experience working closely with research and ML engineering teams as a platform operator
• Familiarity with CI/CD tooling for ML workflows, such as cloud-native pipeline services, GitHub Actions or equivalent
• Awareness of large model training characteristics, including memory footprint, compute scaling and parallelisation strategies
• Familiarity with infrastructure-as-code tooling such as Terraform or cloud-native equivalents
• Familiarity with biomedical AI workloads, such as training foundation models on large-scale multimodal data

Responsibilities:

• Ensure experiment tracking and model registry systems are used effectively across the AI Accelerator, with consistent and correct logging of training and fine-tuning runs and model artefacts registered with full provenance
• Configure, run and troubleshoot distributed training and fine-tuning jobs, ensuring efficient use of available compute and resolving job-level failures
• Participate in structured model handovers with ML engineers, reviewing and signing off documentation before accepting full operational ownership of shipped models
• Deploy, monitor and manage model serving endpoints, making technical decisions about serving configurations to meet downstream performance requirements
• Take full operational ownership of models in production, managing monitoring, retraining and lifecycle end to end
• Uphold MLOps standards and practices across the AI Accelerator, contributing to their evolution based on operational experience and keeping teams current with relevant advances in MLOps tooling

Technologies:

• AI
• CI/CD
• Cloud
• Fine-tuning
• GitHub
• Machine Learning
• MLflow
• MLOps
• Model Serving
• Model Training
• PyTorch
• Terraform
• Support

More:

We are the AI Accelerator, based in London and part of Computational Innovation, a global organisation spanning computational biology, human genetics, data excellence and AI. Our mission is to build production-quality AI capabilities that deepen understanding of disease biology and increase the probability of success. We apply neural-based methods across biomedical data to integrate multimodal sources, infer biological relationships and embed causal thinking into what we build. This is a hybrid role with approximately 3 days a week in the office. We are recognised as a Top Employer in the UK, reflecting our commitment to building an exceptional workplace through strong people practices and supportive HR policies.

last updated 34 week of 2026