Staff Machine Learning Engineer - Ops

About the role

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

🛠️ About our Model Integration and Release Team

Our Model Integration and Release team owns the engineering release process for Wayve’s driving models. When research and engineering teams develop new architectures, features, or data changes, we validate that they meet our quality, safety, and performance standards before they reach customers. The team works across ML engineering, AI Platform, evaluation, and CI/CD to protect the model baseline while continually improving how reliably and efficiently we deliver models.⁠

🧠 Your day-to-day

- Define and evolve Wayve’s model development and release processes, including branching strategies, release cadence, configuration management, and quality gates.

- Review proposed model, architecture, code, and metric changes to ensure the implementation matches its intended outcome.

- Assess evaluation results and determine whether releases meet Wayve’s quality and safety standards.

- Identify delivery bottlenecks and work across teams to address their root causes.

- Balance speed and rigour, making informed decisions about when to accelerate delivery and when a release needs further validation.

🧩 What you’ll be working on

- Release pipelines covering the full ML training and delivery lifecycle.

- Automated checks and tooling that identify issues earlier in development.

- Reliable evaluation methods for assessing model changes and release readiness.

- CI/CD workflows that streamline model integration and delivery.

- Monitoring and observability that improve confidence in production releases.

- Engineering standards and operational processes that enable teams to deliver high-quality ML systems at scale.

🙌 You should apply if

- You have significant software integration and release experience, including branching strategies, release cadences, and configuration management.

- You understand ML training, model lifecycles, and the infrastructure required to deliver models reliably.

- You can review someone else’s model, architecture, or code changes and challenge decisions that do not align with their stated intent.

- You have experience with ML tooling and technologies such as PyTorch, TensorRT, model registries, quantisation, or model deployment.

- You are comfortable working with CI/CD systems and GitHub Actions.

- You take ownership of ambiguous, cross-team problems and prefer fixing underlying processes rather than repeatedly patching individual releases.