Staff ML Platform Engineer - AD/Adas

TEAM

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.

WHO ARE WE LOOKING FOR?

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.

RESPONSIBILITIES

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 Nihonbashi (Japan) office three days per week
Work cross functionally to align on the target architecture of our systems and optimize processes & systems globally
Drive best engineering practices across the organisation

MINIMUM QUALIFICATIONS

BSc / BEng (MS / PhD nice-to-have) in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience
10+ years of experience with data structures, algorithms, design patterns, and software engineering best practices
4+ years of experience with UNIX-based systems (Linux or similar), Python, and PyTorch/Tensorflow
4+ 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
Experience with scaling ML training and fixing the typical issues found at large scale
Experience with Docker and CI systems such as GitHub Actions
Business-level proficiency in English, able to write technical documents (e.g. for software documentation)

NICE TO HAVES

4+ years of experience with Apache Spark, Airflow, Flyte, Flink, Ray, or similar ML pipelines technologies
4+ years using modern systems programming languages (e.g., Rust and/or C++) and a modern build system (preferably Bazel), and systems-level debugging knowledge, in a professional environment
Experience as a Software Architect or Senior Manager
Experience with SIMD/SIMT parallelism, GPU programming, multithreading
Experience with Terraform, AWS, Observability, and Kubernetes in production
Experience with Google Big Query, Snowflake or AWS Redshift in production
Experience in optimizing deep-learning models towards specific hardware targets
Experience in self-driving, robotics, computer vision, or motion planning
Experience working in a fast-paced environment, collaborating across teams and disciplines
Business-level proficiency in Japanese