Senior ML Platform Engineer - AD​/ADAS

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.

At Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. Our work involves a variety of challenges, such as analyzing petabytes of multimodal driving data, solving optimization problems, 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 team is looking for a skilled Software Engineer or Machine Learning Engineer to join the Machine Learning Platform team, and work in close collaboration with and/or embedded in our ML teams to accelerate and scale up our overall ML engineering activities, enabling shipping Perception, Prediction and Planning ML models and our AD/ADAS stack to millions of Toyota customer vehicles.

We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity, and able to leverage cutting‑edge technologies to solve real‑world problems while also considering ML engineers’ productivity and cost efficiency. We are looking for individuals who exhibit a "giver" mindset, consistently seeking opportunities to assist their colleagues while maintaining a strong focus on delivering solutions to production.

Design, build, maintain, optimize and support the ML Platform’s systems and tools for perception, prediction, and planner development, allowing numerous ML engineers to effectively & efficiently iterate on dataset curation, ML modeling, training, evaluation and deployment of ML models into our functionally safe AD/ADAS stack, shipped in millions of Toyota vehicles.

Develop user‑friendly tooling, frameworks and libraries to support the overall ML engineering effort, from ML modeling, to tracking performance metrics and introspecting failure modes.

Build and maintain efficient dataset generation, cloud training and evaluation pipelines.

Develop and review code with other ML and ML Platform engineers to facilitate rapid incremental improvements.

Optimize the current processes, tooling and supporting infrastructure to accelerate the overall ML engineering effort, and contribute to the long term strategy for several of our systems and products.

Work in a high‑velocity environment and employ agile development practices.

Work in a hybrid workspace, with the requirement to be present in our Palo Alto (USA) office three days per week.

BSc / BEng (MS / PhD nice‑to‑have) in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience.

5+ years of experience with data structures, algorithms, design patterns, and software engineering best practices.

2+ years of experience with UNIX‑based systems (Linux or similar), Python, and PyTorch/Tensorflow.

2+ years of experience in the full MLOps cycle covering data cleansing, data sampling, data curation, pre‑processing, efficient data loading, distributed training, testing, evaluation, deployment, inference optimization and deployment in the cloud and on edge compute platforms.

Expe…