Senior Machine Learning Engineer, End-to-end Autonomy

About Woven By Toyota

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society. Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.

Job Summary

At 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.

Key Responsibilities

• Own 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.
• Resolve ambiguities and address uncertainties arising from complex projects involving multiple teams and legacy codebases.
• Enable and help other colleagues on the team to be more effective through leading by example when it comes to writing high-quality code, being rigorous with Machine Learning experimentation and knowledge sharing.
• Collaborate closely with stakeholders from multiple teams in different time-zones to define interfaces and requirements for an end-to-end stack.

Requirements

• MS, or higher degree, in related field, or equivalent industry experience.
• Professional experience with ML frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred).
• Knowledge of debugging and profiling deep neural networks on NVIDIA CUDA stack.
• Experience in state of the art architectures for object detection.