Staff Machine Learning Engineer - Ops
wayve · London, England ·
Overview In this role, you own and improve Wayve's model integration and release processes, ensuring quality, safety, and performance before deployment. You collaborate across ML, AI Platform, and CI/CD to maintain a reliable baseline while accelerating delivery where appropriate.
You'll shape release pipelines, evaluation methods, and tooling that scale ML deployments, contributing to a safer, more capable autonomous driving system. This is a hands-on role focused on cross-functional execution and process improvement.
Pay / Benefits competitive salaries equity participation relocation support and visa sponsorship hybrid working with core hours learning and development budgets comprehensive health and wellbeing benefits Responsibilities Define and evolve release processes, including branching, cadence, configuration management, and quality gates Review model, architecture, code, and metrics to ensure alignment with intent Assess evaluation results to validate quality and safety standards for releases Identify bottlenecks and collaborate across teams to fix root causes Balance speed and rigour to decide when to accelerate or validate further Manage release pipelines spanning ML training and delivery lifecycle Develop automated checks and tooling to catch issues early Establish reliable evaluation methods for release readiness Implement CI/CD workflows to streamline model integration and delivery Enhance monitoring and observability for production releases Define engineering standards and operational processes for scalable ML systems Key requirements Significant software integration and release experience including branching strategies and cadence Understanding of ML training, model lifecycles, and delivery infrastructure Ability to review others' model/architecture/code changes and challenge misaligned decisions Experience with ML tooling such as PyTorch, TensorRT, model registries, quantisation, or model deployment Comfort with CI/CD systems and GitHub Actions Ownership mindset for cross-team, ambiguous problems and root-cause fixes Quality-first mindset with good judgement on when speed matters Clear communication and collaboration across research, engineering, platform, evaluation, and product teams strong cross-functional collaboration clear communication ownership and accountability PyTorch TensorRT model registries