About the role

Technical lead for world foundation models in autonomous driving at Woven by Toyota. Driving innovation in AI and robotics to redefine mobility and enhance safety.

Responsibilities

Lead the design, development and benchmarking of state-of-the-art world foundation models for autonomous driving, ranging from data strategy, multistage training, model selection, and eventual deployment and integration with onboard and offboard applications.
Architect visually realistic simulators to evaluate full end-to-end autonomy stack behavior, from simulating sensors to policy rollouts, across a diverse range of scenario conditions.
Research and implement cutting-edge approaches across domains (reinforcement learning, probabilistic & generative modeling, scene representations, sensor fusion, temporal reasoning) and validate their effectiveness in simulation and through real-world driving performance.
Align efforts across various company-internal teams as well as TRI, providing technical mentorship and fostering a collaborative, high-trust engineering culture across organizational boundaries, influencing technical decisions across the partnership, and possibly co-authoring publications for premier conferences and journals.
Increase the scalability of ML pipelines to support the training and inference of large foundation models, and to optimize edge deployment of state-of-the-art architectures.
Curate scenarios, develop system introspection capabilities, and establish frameworks for understanding model behavior and performance at scale.

MS or PhD in computer vision, ML, robotics, or related quantitative fields.
7+ years of professional experience with computer vision, ML, or applied science.
Strong hands-on experience with foundation models, world models, generative AI, multimodal transformers, diffusion, VLAs, or large end-to-end behavior models for robotics or autonomy.
Expertise in PyTorch (preferred), JAX, or TensorFlow; strong Python and C++ skills.
Strong understanding of temporal/sequential modeling, probabilistic modeling, reinforcement learning, Bayesian inference, state-space models, and uncertainty quantification.
Strong understanding of 3D perception, multi-view geometry and sensor fusion.
Hands-on experience with large-scale distributed training, ML workflows (data curation, training, evaluation, deployment), and inference optimization.
Knowledge of debugging, profiling and deploying deep neural networks with NVIDIA tooling (CUDA, Nsight, TensorRT) and ONNX.
Experience with simulation platforms (e.g., CARLA, Applied Intuition, Nvidia DriveSim, etc.), their internal principles and their integration into autonomous system workflows.
Proven track record of leading large, multi-person technical projects and influencing technical direction across organizations, as well as strong communication skills.