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Technical Lead Manager, Synthetic Data

Wayve
Location
London
Posted
12 days ago
Department
Simulation, Evaluation, Validation
What they actually want (must-haves)
  • 5+ years of experience in ML engineering or applied research roles, with a track record of training and shipping neural networks
  • 4+ years of people management experience, including direct reports and cross-functional project ownership
  • Deep knowledge of generative modelling applied to video or other high-dimensional temporal data
  • Hands-on experience with video, generative or world models
  • Working knowledge of cameras and 3D geometry
  • Evidence of closing the loop: taking generated or simulated data into a trained downstream model
Nice to have
  • Experience in AVs, robotics, simulation, or other embodied AI domains
  • Distillation, few-step sampling, KV caching, or other inference-speed work on large generative models
  • Reward models, offline RL, or closed-loop evaluation of driving policies
  • Productionising research: Flyte/Ray/Spark-style jobs, dataset lineage, training mix configuration; cloud GPU fleets
  • Strong publication record or contributions to open-source ML tooling
What the job really is

As a Technical Lead Manager for Synthetic Data at Wayve, you will lead a team focused on reducing reliance on real-world data for autonomous vehicle training by utilizing generative world models. Your role involves setting technical direction, managing team dynamics, and ensuring the integration of synthetic data into the broader training stack. You will also engage in hands-on technical contributions, optimize processes, and mentor team members in a fast-paced, innovative environment.

Benefits
  • Hybrid working policy combining office and remote work.
Things to weigh
  • Role emphasizes leadership and mentoring over hands-on technical work, which may not suit those preferring direct technical problem-solving.
  • Candidates should be comfortable with high-ambiguity projects and evolving priorities.
  • Experience with generative modelling must include downstream metrics, not just theoretical knowledge.
Job score3/5
Benefits1/5
Freshness5/5
Career value4/5
Role clarity5/5
Pay transparency0/5

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