Staff ML Engineer — ML Ops & Release

OpenDigital Limited; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work policy

London office collaboration

Job summary

Wayve, London-based, is seeking a Staff Machine Learning Engineer (Ops/Release) to define and enforce release gates across complex model training phases. You will drive ML delivery pipelines, review releases, and partner with platform teams to optimize tooling for reliable model delivery and evaluation.

This hybrid role combines in-office collaboration in London with remote work, guarding model baselines and ensuring high safety standards in autonomous driving research.

Qualifications

• Full system thinker with experience introducing operational processes to drive engineering excellence.
• Strong ML Ops, model registry and ML lifecycle experience.
• Deep technical depth in ML training and deployment considerations.
• Strong understanding of ML code infra and best practices — PyTorch, TensorRT and quantisation.
• Proficient CI/CD experience and GitHub Actions usage.
• Clear communicator with collaborative mindset.

Responsibilities

• Collaborate with ML engineers, data engineers and product teams to deliver features end to end.
• Review release content to confirm quality and safety before ships.
• Identify bottlenecks in the ML delivery pipeline and drive fixes that improve speed and maintain quality.
• Collaborate with AI Platform teams to define checks and automation for early issue detection.
• Work with CI/CD teams to adapt workflows and streamline model delivery.
• Collaborate with evaluation teams to ensure reliable evaluation methods and drive new methodologies.
• Stay up to date with the latest MLOps practices and bring improvements into the workflow.

Skills

Full system thinker

ML Ops

ML lifecycle

ML training depth

ML code infra

CI/CD / GitHub Actions

Communication

Job description

Wayve, London-based, is seeking a Staff Machine Learning Engineer (Ops/Release) to define and enforce release gates across complex model training phases. You will drive ML delivery pipelines, review releases, and partner with platform teams to optimize tooling for reliable model delivery and evaluation.

This hybrid role combines in-office collaboration in London with remote work, guarding model baselines and ensuring high safety standards in autonomous driving research.