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Wayve is building the AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn from real-world experience. The Model Integration and Release team owns the engineering release process for Wayve's driving models, ensuring new architectures, features, and data changes meet quality, safety, and performance standards before reaching customers.
In this role, you will define and evolve Wayve's model development and release processes, including branching strategies, release cadence, configuration management, and quality gates. You'll review proposed model, architecture, code, and metric changes to ensure implementations match intended outcomes. You'll assess evaluation results to determine whether releases meet safety and quality standards, identify delivery bottlenecks, and work cross-functionally to address root causes. A key part of the role is balancing speed and rigor—making informed decisions about when to accelerate delivery and when further validation is needed.
You'll work 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, CI/CD workflows that streamline integration and delivery, monitoring and observability to improve confidence in production releases, and engineering standards and operational processes that enable high-quality ML systems at scale.
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company is not fully structured—much of how it works is still being written, and you'll help shape it. The interview process includes a recruiter screen (30 min), competency interview with hiring manager (30 min), deep-dive technical interviews covering ML, PyTorch debugging, and tech leadership (3 hours total), and a final mission & values alignment interview (1 hour).
REQUIREMENTS:
- Significant software integration and release experience, including branching strategies, release cadences, and configuration management
- Understanding of ML training, model lifecycles, and infrastructure required to deliver models reliably
- Ability to review model, architecture, or code changes and challenge decisions that don't align with stated intent
- Experience with ML tooling and technologies such as PyTorch, TensorRT, model registries, quantization, or model deployment
- Comfort with CI/CD systems and GitHub Actions
- Ownership mindset for ambiguous, cross-team problems; preference for fixing underlying processes rather than patching individual releases
- Strong quality mindset combined with judgment to recognize when speed matters
- Clear communication and effective collaboration across research, engineering, platform, evaluation, and product teams
Note: The posting encourages applications even if you don't meet every requirement, particularly if passionate about autonomy and keen to learn.
