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About the Role
This role leads Synthetic Data within Simulation, aiming to dramatically reduce dependency on expensive and time-consuming on-road data collection by turning generative world models into a production engine for training-grade experience. The team focuses on post-training GAIA-class world models for synthetic-data capabilities, running generation at scale, landing synthetic data in training, and expanding coverage to new vehicle platforms and scenarios.
Responsibilities
- Architect the future technical direction for post-training and conditioning world models for synthetic-data capabilities.
- Own the end-to-end generation, evaluation, and training system.
- Lead from the front on key components, codebases, and experiments.
- Drive inference optimization, valid-generation rate, and self-serve workflows.
- Challenge assumptions about where synthetic data pays off and champion bold ideas.
- Lead a high-performing, cross-functional team of ML engineers and applied scientists.
- Drive quarterly planning and execution in a high-ambiguity environment.
- Collaborate with world-model researchers, platform and infra engineers, driving-model owners, and evaluation teams.
- Manage upwards and laterally to align team goals with company priorities and OEM program timelines.
- Grow and structure a resilient team by hiring top talent, designing effective operating models, and fostering a sense of belonging.
- Cultivate a strong, inclusive culture rooted in scientific rigor, collaboration, and curiosity.
- Coach and mentor team members, tailoring growth plans to individual strengths and aspirations.
- Lead by example through technical engagement and clear feedback.
- Navigate the team through evolving research priorities and fast-moving execution, maintaining stability and trust.
Requirements
- 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 (e.g., video generation, novel-view synthesis, neural rendering, controllable generation).
- Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps and reprojection).
- 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 (multi-GPU jobs, workflow orchestration, large video artefacts) and making that path reliable.
- Strong Python and PyTorch engineering fundamentals.
- Experience building research-grade production tools.
- Excellent communication skills and a passion for coaching and mentoring others.
- Ability to balance technical depth with people leadership.
- Ability to know when to lead from the front and when to empower the team.
- Ability to embrace ambiguity and help the team make sense of it, keeping clarity and momentum through uncertainty.
- Experience in AVs, robotics, simulation, or other embodied AI domains (nice to have).
- Experience with multi-sensor driving data (video, telemetry; LiDAR a plus) (nice to have).
- Experience with distillation, few-step sampling, KV caching, or other inference-speed work on large generative models (nice to have).
- Experience with reward models, offline RL, or closed-loop evaluation of driving policies (nice to have).
- Experience with productionizing research (e.g., Flyte/Ray/Spark-style jobs, dataset lineage, training mix configuration; cloud GPU fleets, distributed training) (nice to have).
- Strong publication record or contributions to open-source ML tooling (nice to have).
- Previous experience in startup-like or high-ambiguity environments (nice to have).
Skills
- Machine Learning Engineering
- Applied Research
- Neural Network Training and Shipping
- People Management
- Cross-functional Project Ownership
- Generative Modelling (Diffusion, Flow Matching, Autoregressive, VAEs)
- Video Generation
- Novel-view Synthesis
- Neural Rendering
- Controllable Generation
- Camera and 3D Geometry
- Python
- PyTorch
- Production Tool Development
- Coaching
- Mentoring
- Communication
- Technical Leadership
- Inference Optimization
- Workflow Orchestration
- Autonomous Vehicles (AVs)
- Robotics
- Simulation
- Embodied AI
- Multi-sensor Data Processing
- Distillation
- Few-step Sampling
- KV Caching
- Reward Models
- Offline RL
- Closed-loop Evaluation
- Dataset Lineage
- Cloud GPU Fleets
- Distributed Training
Location
- London
Work Type
- Full-time
- Hybrid
Experience Level
- 5+ years in ML engineering or applied research
- 4+ years in people management
About the Company
- Simulation is advancing our end-to-end autonomous driving research.
- The team’s mission is to accelerate our journey to AV2.0 by incubating capabilities that become company-level advantages.
- GAIA, our generative world models, and the synthetic data they produce, are one of those.