Staff ML Ops Engineer — Release & Delivery

Wayve; Greater London; Hybrid; GBP 110,000 - 160,000; Full time

Job summary

Wayve in London is hiring a Staff Machine Learning Engineer (Ops/Release). You will own the release gates across the ML delivery pipeline, setting standards and validating each training phase before progression.

Collaborate with ML engineers, data teams and CI/CD to streamline model delivery, review changes, identify bottlenecks, and elevate evaluation methods for safer deployments. This high‑trust role requires deep ML training knowledge and clear communication in a hybrid London office.

Qualifications

• Strong ML Ops experience and lifecycle ownership.
• Deep technical depth in ML training and deployment.
• Proficiency with PyTorch and model deployment tooling.
• Experience with CI/CD pipelines and GitHub Actions.
• Excellent written and verbal communication skills.

Responsibilities

• Collaborate with ML engineers, data engineers and product teams to deliver features end to end.
• Review release content — model and metric changes, evaluation results — to confirm everything meets Wayve’s quality and safety standards before it ships.
• Identify bottlenecks in the ML delivery pipeline and drive fixes that improve speed without compromising quality.
• Collaborate with AI Platform teams to ensure tooling meets our delivery needs, defining and building the checks and automation that catch issues earlier.
• Collaborate with CI/CD teams to adapt workflows and streamline model delivery.
• Collaborate with evaluation teams to ensure evaluation methods are reliable, identify gaps, and drive new methodology for evaluating our models.
• Stay up to date with the latest in MLOps practices and tools and bring improvements into the workflow.

Skills

ML Ops

Model registry

ML lifecycle

ML training

PyTorch

TensorRT

Quantisation

Model deployment

CI/CD

Github Actions

Communication

Job description

Wayve in London is hiring a Staff Machine Learning Engineer (Ops/Release). You will own the release gates across the ML delivery pipeline, setting standards and validating each training phase before progression.

Collaborate with ML engineers, data teams and CI/CD to streamline model delivery, review changes, identify bottlenecks, and elevate evaluation methods for safer deployments. This high‑trust role requires deep ML training knowledge and clear communication in a hybrid London office.