Model Release Engineer

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 builds the platform that moves Wayve’s AI Driver models from promising new features through training, simulation, on-road testing and release. Working across the full model lifecycle, the team connects systems and teams to create a scalable, reliable and transparent path to production—helping Wayve deploy new models faster and with greater confidence. 🧠 Your day-to-day Design and build Python services and microservices that orchestrate the end-to-end model-release workflow. Integrate feature candidates into release branches and automate readiness checks before training begins. Connect with training platforms and APIs to coordinate behavioural-cloning, reinforcement-learning and subsequent training steps. Trigger simulation and on-road tests, collect results and surface quality gates, approvals and release status. Productionise services on Azure and Kubernetes, improving availability, scalability, performance, monitoring and alerting. Use AI coding agents to investigate issues, automate manual work and accelerate delivery. 🧩 What you’ll be working on A trusted, end-to-end platform spanning feature integration, training, simulation, on-road testing, approvals and model promotion. Distributed workflows and APIs connecting systems owned by Model Engineering, MLOps, Measurement, Evaluation, Simulation, Operations and Release Management. Reliable approval gates, observability and operational tooling that provide clear visibility into model candidates and their progress. Cloud-native services running on Azure and Kubernetes, with meaningful metrics, logging, tracing, monitoring and alerting. Platform UI workflows, including React-based experiences where useful. Agentic automation that reduces manual engineering effort and increases the team’s delivery capacity. 🙌 You should apply if You have strong software or platform engineering experience building reliable production services or microservices, ideally in Python. You have hands-on experience with Kubernetes, cloud-native infrastructure and production service operations; Azure experience would be an advantage. You have strong system-design skills across distributed workflows, APIs, orchestration and multi-system integrations. You have established observability using meaningful metrics, logging, tracing, monitoring and alerting. You can collaborate effectively across organisational boundaries, agree dependable interfaces and work through conflicting priorities. You use AI coding agents or LLM tools confidently and practically in your day-to-day engineering workflow. Experience with MLOps, model pr