Software Engineer, ML Platform (Internship)

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.

At Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. Our work includes a diverse array of challenges and activities, such as analyzing petabytes of multimodal driving data, solving optimization problems in computer vision, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for Perception, Prediction, and Motion Planning. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications.

The Behavior team builds the machine learning training and deployment ecosystem for AD/ADAS. You will be embedded within the Automated and Assisted Driving team and collaborate closely with Autonomy ML engineers working on Perception and Planning. Our mission is to design scalable, reliable, and cost‑effective ML infrastructure that enables rapid iteration and deployment of high‑quality ML models, from large‑scale data curation and distributed training, to push‑button deployment in production.

We are seeking motivated software interns with a strong interest in ML systems and MLOps. The ideal candidate has hands-on experience training machine learning models and is interested in improving the infrastructure that enables ML research and production at scale.

This role is well suited for candidates who want to work at the intersection of software engineering and machine learning. Interns in this position will contribute to well‑scoped infrastructure projects and help identify and address bottlenecks in dataset creation, distributed training, and model evaluation pipelines.

The position offers close collaboration with senior engineers and ML practitioners, regular technical feedback, and the opportunity to influence core platform components that are used daily by AD/ADAS ML engineers. Successful candidates will gain exposure to production‑grade ML infrastructure and make measurable improvements to the reliability, scalability, and efficiency of the ML development lifecycle.

・We are an equal opportunity employer and value diversity.

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