Technical Lead Manager, Synthetic Data

The role 

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.

What this team is solving:

This role leads Synthetic Data within Simulation. The team exists to dramatically reduce our dependency on expensive and time-consuming on-road data collection by turning generative world models (models like GAIA-3) into a production engine for training-grade experience. When we can restage real driving onto a camera rig that does not exist yet, rewrite ego motion to create scenarios we have never encountered, and land that data in the same training stack we use for real driving, we can train, evaluate and deploy on vehicles and in geographies we have barely collected from.

These are some areas the workstream is focused on:

• Post-training GAIA-class world models for synthetic-data capabilities: rig transfer (new camera and vehicle embodiments), pose transfer (rewritten ego trajectories), and related conditioning on geometry, calibration and actions.
• Running generation at scale with clear lineage from the model and settings that produced them.
• Landing synthetic data in driving-model training and proving its impact on suite and on-road metrics.
• Expanding coverage to new vehicle platforms and safety-critical scenarios, including OEM bring-up and features
• Modify existing data in a controllable and scalable way to create new scenarios or create new ones from scratch.

Where you’ll have impact:

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 would we know?
• How quickly can we deploy autonomous driving in a geography where we’ve never collected AV data?

Key responsibilities

Technical contributions & leadership

As a technical leader you will:

• 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.

Team management & cross-functional execution

As a people and execution leader you will:

• Make things happen — lead a high-performing, cross-functional team of ML engineers and applied scientists working across generative modelling, generation infrastructure and training. Drive quarterly planning and execution in a high-ambiguity environment where the target moves.
• 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. Manage upwards and laterally to align your team’s goals with company priorities and OEM programme timelines.
• 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. Cultivate a strong, inclusive culture rooted in scientific rigour, collaboration and curiosity.
• Level up — coach and mentor team members, tailoring growth plans to individual strengths and aspirations. Lead by example through technical engagement and clear feedback.
• Champion change — navigate your team through evolving research priorities and fast-moving execution, maintaining stability and trust through uncertainty.

About you

In order to set you up for success as a Tech Lead Manager at Wayve, we’re looking for the following skills and experience.

Essential

• 5+ years of experience in ML engineering or applied research roles, with a track record of training and shipping neural networks — not only operating data platforms.
• 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 — for example video generation, novel-view synthesis, neural rendering, or controllable generation.
• Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps and reprojection) and why they break generation or downstream training.
• Evidence of closing the loop: taking generated or simulated data into a trained downstream model and measuring impact through mix ratios, ablations and failure analysis.
• 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, and experience building research-grade production tools.
• Excellent communication skills and a passion for coaching and mentoring others.
• You balance technical depth with people leadership. You know when to lead from the front and when to empower your team.
• You embrace ambiguity and help your team make sense of it, keeping clarity and momentum through uncertainty.

Nice to have

• Experience in AVs, robotics, simulation, or other embodied AI domains, and with multi-sensor driving data (video, telemetry; LiDAR a plus).
• 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 (Azure/AWS/GCP) and distributed training.
• Strong publication record or contributions to open-source ML tooling.
• Previous experience in startup-like or high-ambiguity environments.

This role might not be for you if:

• You’re at your best when solving complex technical problems hands-on, rather than leading through others via mentoring and team support.
• You are mainly managing teams and no longer hands-on in technical projects, and have no desire to get in the weeds or in the code again.
• Your generative modelling experience stops at the checkpoint — you haven’t taken generated data through to a downstream model’s metrics, and don’t want to.
• You aren’t comfortable working on high-ambiguity, research-adjacent projects with moving targets.
• You haven’t yet managed a team with direct reports through full-cycle planning, delivery, and feedback loops.

This is a full-time role based in our office in London.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.