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 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
NICE TO HAVES
- 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

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