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About the Role
This role leads Synthetic Data within Simulation, aiming to dramatically reduce dependency on 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 driving-model training, and expanding coverage to new vehicle platforms and safety-critical scenarios.
Responsibilities
- Architect the future technical direction for post-training and conditioning world models for synthetic-data capabilities.
- Own the end-to-end loop, ensuring generation, evaluation, and training remain one 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 researchers, engineers, and owners across the stack.
- Manage upwards and laterally to align team goals with company priorities and OEM timelines.
- Grow and structure a resilient team by hiring top talent and fostering an inclusive culture.
- Coach and mentor team members, tailoring growth plans to individual strengths and aspirations.
- Navigate the team through evolving research priorities and fast-moving execution.
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, maintaining clarity and momentum.
- 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).
- Not at your best when solving complex technical problems hands-on, rather than leading through others.
- Not mainly managing teams and no longer hands-on in technical projects, with no desire to get in the weeds or in the code again.
- Generative modelling experience stops at the checkpoint — haven’t taken generated data through to a downstream model’s metrics, and don’t want to.
- Not comfortable working on high-ambiguity, research-adjacent projects with moving targets.
- Haven’t yet managed a team with direct reports through full-cycle planning, delivery, and feedback loops.
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
- Data Generation and Evaluation
- Downstream Model Training and Impact Measurement
- Large-Scale Generation/Training Operations
- Python
- PyTorch
- Production Tool Development
- Coaching
- Mentoring
- Technical Leadership
- Ambiguity Management
- Autonomous Vehicles (AVs)
- Robotics
- Simulation
- Embodied AI
- Multi-sensor Driving Data
- Inference Optimization
- Reward Models
- Offline RL
- Closed-loop Evaluation
- Research Productionization
- Cloud GPU Fleets
- Distributed Training
- Open-Source ML Tooling
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
- Founded in 2017, Wayve is the leading developer of Embodied AI technology.
- Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
- Our vision is to create autonomy that propels the world forward.
- Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
- In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions.
- We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
- At Wayve, your contributions matter.
- We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
- Make Wayve the experience that defines your career!
Equal Opportunity
- Wayve is committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
- For US candidates only, please visit E-Verify Notice and Participation and Right to Work.