Lead Behavior Engineer - Machine Learning

Woven by Toyota is the mobility technology subsidiary of Toyota Motor Corporation. Our mission is to deliver safe, intelligent, human-centered mobility for all. Through our Arene mobility software platform, safety-first automated driving technology and Toyota Woven City — our test course for advanced mobility — we’re bringing greater freedom, safety and happiness to people and society. 
Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship. We leverage these complementary strengths to amplify the capabilities of drivers, foster happiness, and elevate well-being.
TEAMWoven 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. 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?The Lead Behavior Engineer - ML will work hand-in-hand with the Behavior Department Head to drive the roadmap, architecture, metrics, and technical strategy to accelerate machine learning solutions related to vehicle behavior. This role requires a system thinker with technical depth in machine learning approaches to complex systems. The applicant should have a strong background in ML algorithms, model architecture, end-to-end strategies, ML frameworks, data curation, and supervised and undervised metrics. The role requires strong communication skills, an aptitude for mentoring junior tech leads, and a split between independent technical execution and cross-organizational problem solving.

RESPONSIBILITIES

• Drive a multi-year technical strategy for accelerating data-driven and deep learning approaches to deploy Behavior software components for AD/ADAS applications, ensuring scalable, high-performance solutions while maintaining best practices for software development and system design.
• Lead the execution of projects by defining efficient engineering processes, mitigating technical risks, and advocating for architectural improvements that enhance system reliability and scalability.
• Collaborate with cross-functional teams  such as Data, Perception, Developer Ecosystem, and  Product Delivery while  influencing technical decisions across the organization to drive innovation, ensure seamless system integration, and improve overall engineering efficiency.
• Develop state-of-the-art models  to deliver desired driving behaviors in various driving scenarios.
• Increase speed of the component- and system-level model iteration while maintaining cost efficiency
• Incorporate data-driven approaches to improve system performance and unlock new capabilities for safety.
• Maintain the functional architecture design and lead review of technical designs
• Drive organizational metrics towards performance, safety, and quality
• Maintain external presence of the department through papers, patents, and presentations
• Assess and mitigate risk to the technical program
• Act in partnership with other Lead Engineers in AD/ADAS to solve high impact cross-functional issues
• Mentor and support engineers, fostering growth through code reviews, knowledge sharing, and collaboration with cross-functional teams to align technical solutions with business goals.
• Design reusable software components as part of an integrated system.
• Understand and fulfill the software practices that produce maintainable code, including simulation, continuous integration, code review, HIL testing, and in-vehicle testing.
• Build component and system level validation strategies that are leveraged to resolve complex interactions between components, increase performance, and evaluate design tradeoffs related to data-driven practices

MINIMUM QUALIFICATIONS

• 10+ years of professional experience with machine learning applications or applied science
• MS or PHD in Machine Learning, Mechanical, Electrical, Computer, Aerospace Engineering, or similar 
• Deep experience with ML system design and verification over a variety of approaches, including supervised/unsupervised learning, reinforcement learning, diffusion policy
• Strong R&D potential in algorithm design, data-driven approaches to safety, and large-scale, complex systems architecture, and requirements-driven development
• Experience with utilizing deep learning frameworks to increase speed and ease of de