Lead Behavior Engineer – Machine Learning

Lead Behavior Engineer – Machine Learning

Requirements

• 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 development
• Experience with temporal data and/or sequential modeling
• Experience with code quality and systems safety processes for production code delivery, with respect to data-driven methodologies and model validation
• Strong, practical understanding of real-time system development, performance issues, testing modalities, and tradeoffs
• Ability to write code in C++ and python
• Ability to lead in globally distributed team
• You are an excellent communicator, skilled collaborator, and principled colleague

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.

Preferred Qualifications

• Published research at top-tier conferences (NeurIPs, CVPR and similar)
• Proven track record of deploying ML models at scale in self-driving or related fields.
• Experience with computer vision (e.g.multi-view geometry, camera calibration, depth estimation, neural radiance fields, gaussian splatting, simultaneous localization and mapping)
• Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control)
• Japanese language skills