Location
London (Hybrid working model with dedicated office spaces and remote work flexibility)

Hours
Full Time

Salary
Negotiable

About the Role
At Wayve, we build embodied intelligence that moves real vehicles safely and power the ecosystem a billion machines will run on in the future. The Staff Machine Learning Engineer - Ops role is part of our Model Integration and Release team, which owns the engineering release process for Wayve’s driving models. This team validates new architectures, features, and data changes developed by research and engineering teams to ensure they meet quality, safety, and performance standards before reaching customers. You will work across ML engineering, AI Platform, evaluation, and CI/CD to protect the model baseline while improving the reliability and efficiency of model delivery.

Your day-to-day responsibilities
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 implementations align with intended outcomes. Assess evaluation results to determine if releases meet quality and safety standards. Identify delivery bottlenecks and collaborate across teams to resolve root causes. Balance speed and rigor by making informed decisions about accelerating delivery or requiring further validation.

Key projects you’ll be working on
- Release pipelines covering the full ML training and delivery lifecycle
- Automated checks and tooling to 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 to improve confidence in production releases
- Engineering standards and operational processes enabling high-quality ML systems at scale

About Wayve
Wayve is pioneering an end-to-end AI platform for autonomous driving that enables vehicles to learn directly from real-world experience. Our AI Driver adapts, generalizes, and improves at scale without relying on hand-coded rules or pre-mapped environments. Our technology navigates complex urban environments with intelligence, precision, and natural flow, unlocking advances in safety and efficiency. We aim to make autonomy universal by building a generalizable driving intelligence that can power any vehicle, anywhere, integrating with global OEM partners and enabling continuous software evolution from L2+ through L4 automation.

Work environment and culture
We operate a hybrid working model with hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. Our culture values ownership, collaboration, and continuous learning. Wayve is committed to diversity, fairness, and inclusion, fostering a respectful environment where all unique skills and perspectives are valued.

Experience
- Significant software integration and release experience including branching strategies, release cadences, and configuration management
- Understanding of ML training, model lifecycles, and infrastructure for reliable model delivery
- Ability to review and challenge model, architecture, or code changes to ensure alignment with intended outcomes
- Experience with ML tooling and technologies such as PyTorch, TensorRT, model registries, quantisation, or model deployment
- Comfortable working with CI/CD systems and GitHub Actions
- Proven track record of taking ownership of ambiguous, cross-team problems and improving underlying processes rather than patching individual releases

About you
- Strong quality mindset combined with good judgment to balance speed and rigor
- Clear communicator and effective collaborator across research, engineering, platform, evaluation, and product teams
- Passionate about autonomy and continuous learning
- Comfortable working in a fast-evolving environment where processes and structures are still being developed

Qualifications
No specific qualifications required; passion for autonomy, learning, and ownership is highly valued. Candidates are encouraged to apply even if they do not meet every requirement.