Machine Learning Engineer en Woven-by-toyota

TeamAt Woven by Toyota, we are at the forefront of developing advanced Machine Learning solutions for autonomous driving. Our team tackles groundbreaking challenges in designing state-of-the-art neural networks, pioneering innovative end-to-end architectures, and advancing ML techniques in perception, prediction, and motion planning. We're passionate about pushing the boundaries of autonomous systems through deep learning and optimization, particularly in complex 3D geometric computer vision scenarios. We're seeking passionate innovators and creative problem-solvers eager to redefine mobility through cutting-edge AI and robotics, contributing directly to shaping the future of self-driving technology.Woven by Toyota is developing a joint project between Toyota Research Institute (TRI) and Woven by Toyota to research and develop a fully end-to-end learned automated driving / ADAS stack. This cross-org collaborative project is synergistic with TRI’s robotics division’s efforts in Diffusion Policy and Large Behavior Models (LBM).



Responsibilities

Support the design and development of ML models or model components for end-to-end autonomous driving: ranging from initial data strategy, design, development, experimentation, evaluation and deployment;

Able to navigate ambiguities and address uncertainties arising from complex projects involving multiple teams and legacy codebases;

Writes high-quality code while being rigorous with Machine Learning experimentation;

Collaborate closely with stakeholders from multiple teams in different time-zones to define interfaces and requirements for an end-to-end stack;

Experience

MS, or higher degree, in related field, or equivalent industry experience

Professional experience with ML frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred)

Experience with data sampling and data curation pipelines for autonomous driving datasets

Experience in state of the art architectures for end-to-end autonomous driving

Experience in ML workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization

Python and C++ experience

Experience with infrastructure for large scale datasets and distributed model training

Experience working with a modern cloud service (AWS, GCP, Azure etc.)

Nice to Have

Hands-on experience with autonomous driving systems

Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling)

Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures)

Knowledge of autonomous driving large scale data curation pipelines

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