Release Manager - AI Models

Job description

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 ENGINEERING TEAMS Our engineering teams bring together ML engineering, evaluation and data science, software platform, testing, and product to turn breakthrough autonomous-driving technology into reliable products. Model releases connect these disciplines, taking AI models from early hypotheses through training, evaluation, and on-road testing to deployment across our vehicle fleet. As our Release Manager — AI Models, you’ll help ensure that a core part of our technology — the AI model — is released reliably, on time, and to the standard required for production. You’ll bring structure and disciplined release practices to the fast-paced, non-deterministic world of ML, building the processes and systems that support clarity, stability, and business goals. 🧠 YOUR DAY-TO-DAY - Own the model release cycle, maintaining a steady and predictable cadence while adapting to changing business priorities. - Coordinate across ML engineering, evaluation and data science, software platform, testing, and product to keep releases moving and resolve dependencies. - Run the gates that promote models from hypothesis → training → evaluation → validation → deployment, making clear what is ready to ship and what needs more work. - Drive investigations into regressions and integration issues, coordinate rollback or roll-forward decisions, and follow through on technical debt and corrective actions. - Be the central point of contact for model-release status, risks, and timelines, giving engineers and leadership the right level of detail. - Run post-release reviews and turn lessons learned into practical improvements across teams. 🧩 WHAT YOU’LL BE WORKING ON: - Release gates and promotion criteria: Define and justify what it takes for a model to progress, balancing performance gains, reproducibility, product requirements, safety, and compatibility with the onboard software stack and vehicle platform. - A release process that works for ML: Build frameworks that connect model development, evaluation, the Wayve onboard stack, and product needs — applying strong release-engineering practices while recognising where hypothesis-driven, non-deterministic systems need a different approach. - Bottleneck identification and improvement: Find constraints in the release lifecycle, implement dashboards and KPIs to make them visible, and proactively escalate gaps. - Tooling and automation: Partner with ML platform and integration teams to shape end-to-end automated release tooling. Define requirements that improve developer experience, release efficiency, and stakeholder communication. - Reliable, stable deployments: Strengthen how teams handle model versions, integration issues, regressions, and recovery so that releases deliver value reliably. 🙌 YOU SHOULD APPLY IF: - You have 5+ years of experience in release management, software engineering, or a related field. - You understand the Software Development Life Cycle (SDLC) and release management practices, including testing strategy, system integration, branching, and versioning. - You have a conceptual understanding of ML fundamentals: what training data is, the difference between training and inference, what a model version or checkpoint represents, and why model validation is probabilistic rather than simply pass/fail. - You have experience with CI/CD pipelines and automation tools such as GitHub, Buildkite, and CLI tools. - You’re skilled at managing releases in complex, fast-moving systems and comfortable adapting processes and tooling as requirements, architecture, and timelines change. - You bring strong problem-solving and decision-making skills, including when release trade-offs depend on limited or ambiguous data. - You communicate clearly with engineers and leadership, helping people understand priorities, risks, and decisions. - You’re pragmatic, methodical, and system-minded. You bring strong release-engineering instincts and are keen to develop the judgment to know where ML systems require a different approach. It’s a bonus if you also bring: - Experience in AI/ML, autonomous vehicles, or robotics. - Familiarity with MLOps methodologies and tooling. - Experience with ML evaluation practices and tooling, especially for physical systems — including simulation, model leaderboards, and statistically powered experiments such as A/B or shadow-mode testing. - Awareness of model hardware and software dependencies. - A BSc or equivalent in Computer Science, Engineering, or a related technical discipline. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and