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About the Role

Own the full lifecycle of AI model releases, from hypothesis to deployment, balancing ML's non-deterministic nature with structured release practices. This role requires a pragmatic, methodical, and system-minded individual who understands how model releases integrate into the broader engineering, evaluation, and product ecosystem.

Responsibilities

Own the model release cycle, maintaining a steady and predictable cadence for shipping new models while adapting to business priorities.
Define and manage gates for model promotion from hypothesis through deployment, justifying promotion criteria based on performance, reproducibility, product requirements, safety, and integration.
Drive investigations and follow-up actions on regressions, integration issues, and technical debt; manage rollback/roll-forward of model versions.
Identify constraints in the model-release process and drive improvements, implementing dashboards and KPIs for visibility.
Build processes and frameworks that support clarity, stability, and business goals within the model development, evaluation, and product systems.
Partner with ML platform and integration teams to influence end-to-end automated release tooling and define requirements for improved developer experience and efficiency.
Collaborate with ML engineering, evaluation, data science, software platform, testing, and product teams to ensure successful and stable model releases.
Act as the central point of contact for model-release status, risks, and timelines, keeping stakeholders informed.
Run post-release reviews and drive corrective actions across teams.

Requirements

5+ years in release management, software engineering, or a related field.
Strong understanding of the Software Development Life Cycle (SDLC) and release management practices, including testing strategy, system integration, branching, and versioning.
Conceptual understanding of ML fundamentals (training data, training vs. inference, model versioning, probabilistic validation).
Experience with CI/CD pipelines and automation tools (e.g. GitHub, Buildkite, CLI tools).
Skilled at managing releases in complex, fast-moving systems.
Comfortable operating with high ambiguity and adapting release processes and tooling as needed.
Strong problem-solving and decision-making ability, especially in release trade-offs with limited or ambiguous data.
Good communication skills, comfortable working with engineers and presenting to leadership.
Experience in AI/ML, autonomous vehicles, or robotics (desirable).
Familiarity with MLOps methodologies and tooling (desirable).
Experience with ML evaluation practices and tooling (desirable).
Awareness of model hardware and software dependencies (desirable).

Skills

Release Management
Software Engineering
SDLC
Release Management Practices
Testing Strategy
System Integration
Branching
Versioning
ML Fundamentals
CI/CD
Automation Tools
GitHub
Buildkite
CLI Tools
Complex System Management
Ambiguity Tolerance
Problem-Solving
Decision-Making
Communication
MLOps
ML Evaluation
Hardware/Software Dependencies

Location

London

Work Type

Full-time
Hybrid

Experience Level

5+ years

Education Level

BSc or equivalent in Computer Science, Engineering, or a related technical discipline (desirable)

About the Company

At Wayve, you'll help deliver the value of our breakthrough autonomous-driving technology.