Machine Learning Engineer, Wayve

• ~5+ years of relevant industry, including experience designing and implementing offline evaluation pipelines for ML models (vision, multimodal, or embodied AI)
• Strong software engineering skills, including Python, data processing, and ML tooling
• Hands-on experience with human annotation workflows (task design, QA, coordination) as well as working with internal and external annotation partners
• Deep understanding of metrics and benchmark design to evaluate complex model behavior and a desire to build more
• Ability to collaborate cross-functionally in fast-paced, high-ownership environments

Desirable

• This is a high-impact opportunity to shape how Wayve evaluates its foundation models for embodied AI.
• You'll lead the development of our offline evaluation suite—designing robust, interpretable metrics for tasks ranging from action and perception to language-grounded reasoning.
• Working closely with science, datasets, and infra teams, you'll also drive human annotation efforts to ensure high-quality ground truth data. Your work will directly influence how we understand, trust, and deploy our models in the real world
• Build and scale offline evaluation pipelines for embodied AI models
• Design and implement benchmarks and metrics across vision, language, and driving tasks
• Lead and coordinate human annotation workflows, including quality assurance and task design
• Collaborate cross-functionally with science, datasets, and engineering teams
• Analyze offline metrics and correlate them with online performance to inform deployment readiness