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Wayve is building the leading AI platform for autonomous driving using end-to-end AI that enables vehicles to learn directly from real-world experience. The Model Integration and Release team builds the platform that moves Wayve's AI Driver models from promising new features through training, simulation, on-road testing, and release.
In this role, you will design and build Python services and microservices that orchestrate the end-to-end model-release workflow. Your responsibilities include integrating feature candidates into release branches and automating readiness checks before training begins, connecting with training platforms and APIs to coordinate behavioral-cloning and reinforcement-learning steps, triggering simulation and on-road tests while collecting results and surfacing quality gates and approvals, and productionizing services on Azure and Kubernetes with improved availability, scalability, performance, monitoring and alerting.
You'll work on a trusted, end-to-end platform spanning feature integration, training, simulation, on-road testing, approvals and model promotion. This involves building distributed workflows and APIs connecting systems owned by Model Engineering, MLOps, Measurement, Evaluation, Simulation, Operations and Release Management. You'll establish reliable approval gates, observability and operational tooling that provide clear visibility into model candidates and their progress. The role includes building cloud-native services running on Azure and Kubernetes with meaningful metrics, logging, tracing, monitoring and alerting, as well as platform UI workflows including React-based experiences where useful. You'll also use AI coding agents to investigate issues, automate manual work and accelerate delivery.
Wayve operates a hybrid working model combining in-person collaboration in London offices with focused remote work time. The company has main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. This is a full-time role where you'll help shape how the company works—much of the structure is still being written, and you'll have real ownership in building it.
REQUIREMENTS:
- Strong software or platform engineering experience building reliable production services or microservices, ideally in Python
- Hands-on experience with Kubernetes, cloud-native infrastructure and production service operations; Azure experience is an advantage
- Strong system-design skills across distributed workflows, APIs, orchestration and multi-system integrations
- Established observability using meaningful metrics, logging, tracing, monitoring and alerting
- Ability to collaborate effectively across organizational boundaries, agree dependable interfaces and work through conflicting priorities
- Confident and practical use of AI coding agents or LLM tools in day-to-day engineering workflow
- Experience with MLOps, model productionization, training pipelines, evaluation, simulation or model-release workflows is an advantage
- Front-end development experience, particularly with React, is beneficial
Note: The posting encourages applications even if you don't meet every requirement, particularly if you're passionate about autonomy and keen to learn.
