ML Platform Senior Engineering Manager, Autonomy

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. 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? We are seeking for an experienced, technical, hands-on ML Engineering Manager to lead a group of highly talented and passionate ML Platform engineers, and work in close collaboration with 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.  This role focuses on developing and deploying tooling, automation and infrastructure to support the entire ML software lifecycle, from data loading of previously curated ML datasets and training of ML models at scale, ML models evaluation, and ML models deployment including quantization and conversion to formats supported by our edge devices. The ideal candidate has a “giver” mindset and a deep passion for self-driving technology and its potential global impact. You will use your domain expertise in machine learning, a comprehensive understanding of the model lifecycle, and experience developing tools and pipelines for vehicle perception, localization, prediction, and planning. Prior experience building and deploying real‑world autonomous driving products is essential. Success in this role requires a candidate who thrives in a fast‑paced environment, is eager to push the boundaries to continuously improve efficiency and performance, and has a track record of driving effective cross-functional work across multiple teams and departments. Bring your passion for self-driving technology and help us create the future of mobility for the largest car manufacturer in the world! RESPONSIBILITIES

Define the team’s short‑term and long‑term technical direction while collaborating on broader cross‑functional strategic initiatives.

Initiate and influence cross‑functional teams toward common development goals to drive innovation.

Enable and support your team to be more effective through coaching, leading by example, providing high‑quality code and design‑document reviews, and delivering rigorous reports.

Collaborate with team members to design, develop, deploy, optimize, and evaluate state‑of‑the‑art pipelines and processes for ML model development, testing, and deployment.

Lead the execution of projects by defining efficient engineering processes, mitigating technical risks, and advocating for architectural improvements that enhance system reliability and scalability.

Increase speed of the component and system-level model iteration while maintaining cost efficiency.

Drive organizational metrics towards performance, safety, and quality.

Design reusable software components as part of an integrated system.

Understand and champion software practices that produce maintainable code, including continuous integration, code review, etc.

Work in a globally distributed department (US, Japan, London).

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

MINIMUM QUALIFICATIONS

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

3+ years of experience managing engineering teams, with a focus on technical leadership, team development, and delivering high-impact projects in the automotive industry.

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

Deep understanding of runtime complexity, space complexity, distributed computing, and the application of these concepts in concrete, distributed ML training and evaluation.

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

4+ years of experience in the full MLOps cycle covering data cleansing, data sampling, data curation, pre-processing, 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 Apache Spark, Airflow, Flyte, Flink, Ray, or similar ML pipelines technologies.

Experience working with temporal data and/or sequential modeling.

Experience with Docker and CI systems such as GitHub Actions.

Excellent communication, with the ability to communicate concepts clearly and precisely.

Track record of effective cross-functional collaboration.

Ability to lead within a globally distributed department.

Passionate about self-driving car technology and its potential for humanity.

NICE TO HAVES

Experience as a Software Architect or Senior Manager. 

Experience with deep learning approaches such as supervised/unsupervised learning, transfer learning, multi-task learning, and/or deep reinforcement learning.

Proven track record of deploying ML models at scale in self-driving or related fields.

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 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 deploying and tuning ML models onto custom edge hardware in robotics applications.

Previously worked at, or in close collaboration with ML Research Engineers to deliver a solid software foundation that other ML Engineers can build on.

Familiarity with production-level coding in time-limited task schedules.

Experience in self-driving, robotics, computer vision, or motion planning.

Japanese language skills.