Staff Tech Lead Manager, Machine Learning, Vision Models

Overview

In this Staff Tech Lead Manager role, you will steer Wayve’s Measurement team within AI Evaluation in London, building and productionising offline scene understanding models. You’ll guide four Senior MLEs and set the technical direction to adapt on-vehicle and Foundation Models for offline assessment. Your work supports counterfactual reasoning, coverage analysis, and rare-event mining to inform model development and customer deliverables. This is a high-impact, cross-site leadership opportunity shaping measurement-driven AI for autonomous driving.

Pay / Benefits

• hybrid working policy
• London office
• inclusive workplace culture

Responsibilities

• Lead and grow the team: directly manage four Senior MLEs in London; hire across MLE, SWE, and data science profiles as scope grows; develop senior ICs.
• Own the technical direction: define architecture for adapting shared on-vehicle models and Wayve Foundation Models for offline measurement.
• Own the roadmap: drive sprint, quarterly, and annual planning; translate business goals into technical programs.
• Drive production quality: establish rig-agnostic ML system practices with clean interfaces; maintain architectural quality.
• Accelerate the development loop: shorten the cycle from driving-model iteration to actionable feedback.
• Partner and anticipate: align roadmaps with UK/US teams; assess capability gaps 6–24 months out and plan investments.

Key requirements

• 8+ years in ML engineering with hands-on computer vision experience on camera and/or lidar data; proven production ML systems from research to customer-facing software.
• 2+ years line-managing or tech-leading senior engineers, including hiring and development.
• Staff-level technical depth in transformer-based and multimodal architectures, foundation models, and large-scale training; ability to review designs and code credibly.
• Proficient in Python and ML frameworks (notably PyTorch); strong judgement of production-grade ML systems.
• Strong cross-functional leadership: aligning roadmaps across teams and geographies; communicating technical context to engineers and leadership.

• strategic thinking
• clear communication
• collaboration across international teams
• transformer-based and multimodal architectures
• foundation models
• large-scale training

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