Description
We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Every role here, whatever the team, plugs into that.
As Tech Lead Manager, you'll lead a high-performing team of machine learning engineers and help us answer questions like: can we train and validate a driving model for a vehicle platform before the fleet exists, can synthetically generated data replace scarce real-world data for training and evaluation, and how quickly can we deploy autonomous driving in a geography where we've never collected AV data?
Your day-to-day:
- Algorithmic and system design: define approach to GAIA generation, system design of the synthetic data pipeline and controllability engine, focus on efficiency and quality.
- Cross-team collaboration: aligning with driving-model owners and with evaluation and safety team on utility and scalability of generated data.
- Mentorship / async review: reviewing a teammate's experimental design, PR, or ablation write-up and giving direct feedback.
- 1:1s and feedback: building rapport and managing your direct reports.
- Up to date with state of the art: keeping current on video generation, camera transfer, distillation , insofar as it changes a checkpoint or a mix we ship.
- Strategy: leading or joining strategic discussions on the synthetic data roadmap, safety case and platform bring-up sequencing.
What you'll be working on:
- Architect the future , set the technical direction for how we post-train and condition world models for synthetic-data capabilities (rig transfer, pose transfer, controllability), holding a high bar for what counts as training-grade generation.
- Own the loop end to end , make sure generation, evaluation and training stay one system: from checkpoint and config, through large-scale GPU inference, to artefacts that land in driving-model training with reproducible lineage.
- Get hands-on when it matters , lead from the front on key components, codebases and experiments.
- Push throughput and yield , drive inference optimisation (distillation, few-step sampling, KV caching, step count), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist in the loop.
- Disrupt thoughtfully , challenge assumptions about where synthetic data pays off, ask sharp questions, and champion bold ideas that move us beyond incremental gains.
- Make things happen , lead a high-performing, cross-functional team of ML engineers and applied scientists working across generative modelling, generation infrastructure and training.
- Align and connect , collaborate with world-model researchers, platform and infra engineers, driving-model owners and evaluation so synthetic data is integrated into the broader stack, not delivered over a wall.
- Architect teams , grow and structure a resilient team by hiring top talent, designing effective operating models, and fostering a sense of belonging regardless of location.
- Level up , coach and mentor team members, tailoring growth plans to individual strengths and aspirations.
- Champion change , navigate your team through evolving research priorities and fast-moving execution, maintaining stability and trust through uncertainty.
You should apply if:
- 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 (diffusion, flow matching, autoregressive, or VAEs) 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 and measuring impact.
- Experience operating generation or training at real scale.
- Strong Python and PyTorch engineering fundamentals.
- Excellent communication skills and a passion for coaching and mentoring others.
Benefits:
- Salaries benchmarked against the market annually
- Meaningful equity, sharing in the ownership and long-term success of Wayve
- Relocation support and visa sponsorship where applicable
- Hybrid working, core hours and the chance to work hands-on in vehicle workshops and labs
- Learning and development budgets with support for training, conferences and growth
- Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more