Machine Learning Engineer (Synthetic Data)
Core
Build, scale, and optimize next-generation world model architectures (GAIA) to generate multimodal synthetic data for accelerating autonomous driving development.
Role type
Senior IC machine learning engineer (generative world models & synthetic data)
Builds
High-throughput generative world models and training-ready synthetic datasets for autonomous vehicles
Domain
Autonomous driving, generative AI, computer vision, 3D geometry
Deliverable
production ML models
Required skills
Python, PyTorch, GPU training/debugging, video/generative/world models (diffusion/flow/autoregressive), multi-camera rig geometry (intrinsics/extrinsics/warps), large-scale workflow orchestration, downstream model impact analysis
Preferred skills
Video diffusion/flow/controllable generation, model distillation/KV caching, AV/robotics/simulation, production ML pipelines (Flyte/Ray/Spark), offline RL/reward models, cloud GPU fleets (Azure/AWS/GCP)
Responsibilities
Post-train and iterate world models for rig/pose transfer; own the generation loop from config to training artefacts; integrate synthetic data into driving model training (behaviour cloning/RL); diagnose geometry/calibration failures; optimize inference throughput and yield; expand coverage to new vehicle platforms and safety scenarios; partner with researchers and platform engineers
Seniority
Senior, hands-on IC