# Release Manager - AI Models **Company**: Wayve **Location**: London **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Automotive **Apply**: https://jobs.ashbyhq.com/wayve/c8056c08-472a-4d64-8d69-a6520083bf80?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_6bf7e9d3-8ab ## Description We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. 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. **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. ## Skills ### Required - release management - software engineering - ML fundamentals - CI/CD pipelines - automation tools ### Nice to have - AI/ML - autonomous vehicles - robotics - MLOps methodologies - ML evaluation practices --- Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/wayve/c8056c08-472a-4d64-8d69-a6520083bf80?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)