Staff Tech Lead Manager, Machine Learning, Vision Models
On-siteLondon, England, United Kingdom
Job Summary
Lead and grow the London-based team of four Senior Machine Learning Engineers, hiring complementary talent across MLE, SWE, and data science profiles. Own the technical direction for adapting shared on-vehicle models and Wayve Foundation Models into robust, scalable scene understanding models that power validation machines. Drive planning at sprint, quarterly, and annual cadences to translate business goals into concrete technical programmes while ensuring rigorous engineering practice for customer-facing deliverables. Accelerate the development loop to reduce time between driving-model iteration and actionable feedback, partnering with on-vehicle modelling and Evaluation teams across the UK and US.
Required Qualifications
- 8+ years in ML engineering, including hands-on computer vision with camera and/or lidar sensor data, and a track record of shipping production ML systems from research through to reliable, monitored, customer-facing software
- 2+ years line-managing or tech-leading senior engineers, including hiring, developing, and retaining strong ICs, with the appetite to keep doing both halves of the TLM role
- Staff-level technical depth that earns the trust of senior MLEs: transformer-based and multimodal architectures, foundation models, and large-scale training, with the ability to review designs and code credibly
- Proficient in Python and ML frameworks (esp. PyTorch), with strong judgement about what production-grade looks like for ML systems
- Strong cross-functional leadership: aligning roadmaps across teams and geographies, and communicating technical context and strategic direction clearly to engineers and senior leadership
- This is a full-time role based in our office in London
- hybrid working policy that combines time together in our offices and workshops
Desired Qualifications
- Knowledge of perception systems and 3D scene understanding for autonomy, such as cuboid detection, lane estimation, depth estimation, and large-scale semantic enrichment of driving scenes
- Experience with offboard or offline models: auto-labelling, model distillation, temporal or world models, or other ways of exploiting compute that on-vehicle systems cannot
- Familiarity with simulation or counterfactual evaluation methods for autonomous systems
- Experience leading or partnering with distributed teams across UK and US time zones
- MS or PhD in Computer Science, Engineering, or a related field
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