Engineering Manager, ML Platform, Behavior

Woven•Palo Alto, CA

16h•$161,000 - $264,500•Hybrid

About The Position

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 Woven by Toyota's AD/ADAS organization is developing the core algorithms for safe and robust driving of complex and uncertain real-world environments. Our teams are contributing to next generation autonomy and ADAS active safety systems through core algorithms, data-driven approaches, and efficient architectures, working across the spectrum of active safety and L2-L4 Autonomy towards production targets with Toyota. The Behavior Department within AD/ADAS is responsible for accelerating ML and AI technology to deliver state-of-the-art and safe vehicle behaviors. Working across the spectrum of active safety and L2-L4 autonomy, the team delivers core technology in the areas of perception, planning, control, and HMI towards production targets with Toyota. WHO ARE WE LOOKING FOR? We are seeking an experienced, technical, hands‑on Engineering Manager to lead a group of highly talented and passionate ML Frameworks engineers. This role focuses on developing and deploying efficient cloud and data curation pipelines, automated training, evaluation and release pipelines, and data introspection tools that accelerate and improve our Behavior model training. The ideal candidate has 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 related to vehicle behavior (planning, prediction, control, HMI). Success in this role requires a candidate who thrives in a fast‑paced environment and is eager to push the boundaries to continuously improve efficiency and performance. Prior experience building and deploying real‑world autonomous driving products and collaborating across multiple departments is essential. You will have the chance to design and implement innovative machine learning pipelines, and help accelerate the release of models for our next-generation autonomous vehicle platform, influencing future Toyota customer vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity.

Requirements

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.
Experience with Python, PyTorch/Tensorflow, and software engineering best practices.
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.
Deep understanding of runtime complexity, space complexity, distributed computing, and the application of these concepts in concrete, distributed ML training and evaluation.
Experience working with temporal data and/or sequential modeling.
Strong communication skills with the ability to communicate concepts clearly and precisely.
Ability to write code in C++ and python.
Ability to lead within a globally distributed department
Excellent communication, skilled collaboration, and principled interactions.
Passionate about self-driving car technology and its potential for humanity.

Nice To Haves

Experience with deep learning approaches such as supervised/unsupervised learning, transfer learning, multi-task learning, and/or deep reinforcement learning.
Experience with Apache Spark, Airflow, Flyte, Flink, Ray, or similar ML pipelines technologies.
Experience 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 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.
Proven track record of deploying ML models at scale in self-driving or related fields.
Familiarity with production-level coding in time-limited task schedules.
Experience in self-driving, robotics, computer vision, or motion planning.
Japanese language skills.

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, 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.