Staff Machine Learning Engineer - Ops
Salary not listed • London, United Kingdom • Full-time
Posted 28d ago
About the job
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The Model Integration and Release team owns the engineering release process for Wayve’s driving models, validating that new architectures, features, or data changes meet quality, safety, and performance standards before they reach customers. This role involves working across ML engineering, AI Platform, evaluation, and CI/CD to protect the model baseline while continually improving how reliably and efficiently models are delivered.
Responsibilities
- Define and evolve 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 implementation matches intended outcomes.
- Assess evaluation results to determine if releases meet quality and safety standards.
- Identify delivery bottlenecks and collaborate with teams to address root causes.
- Balance delivery speed and rigor, making informed decisions about acceleration or further validation.
- Develop release pipelines for the full ML training and delivery lifecycle.
- Create automated checks and tooling to identify issues early in development.
- Implement reliable evaluation methods for assessing model changes and release readiness.
- Streamline CI/CD workflows for model integration and delivery.
- Enhance monitoring and observability to increase confidence in production releases.
- Establish engineering standards and operational processes for delivering high-quality ML systems at scale.
Requirements
- Significant software integration and release experience, including branching strategies, release cadences, and configuration management.
- Understanding of ML training, model lifecycles, and the infrastructure for reliable model delivery.
- Ability to review and challenge model, architecture, or code changes.
- Experience with ML tooling and technologies such as PyTorch, TensorRT, model registries, quantization, or model deployment.
- Comfort working with CI/CD systems and GitHub Actions.
- Proven ability to take ownership of ambiguous, cross-team problems and fix underlying processes.
- Strong quality mindset combined with judgment for when speed is critical.
- Clear communication and effective collaboration across research, engineering, platform, evaluation, and product teams.
Benefits
- Salaries benchmarked against the market annually
- Meaningful equity
- Relocation support
- Visa sponsorship
- Hybrid working
- Core hours
- Chance to work hands-on in vehicle workshops and labs
- Learning and development budgets
- Support for training and conferences