Lead Perception Engineer
@ Woven by ToyotaLead Perception Engineer
This job is still taking applications, but it's been up a while.
About the job
Woven by Toyota is transforming mobility with autonomous driving, AI, and software platforms. The role focuses on leading ML and perception systems to advance vehicle understanding, safety, and efficiency in a collaborative, innovative environment.
Requirements
- 10+ years ML experience
- Deep knowledge of ML algorithms
- Experience with real-time systems
- Proficiency in C++ and Python
- Leadership in distributed teams
Qualifications
- MS or PhD in engineering fields
- Strong research and deployment record
- Excellent communicator and collaborator
Full job description
You’ll collaborate daily with ML researchers, software engineers, robotics engineers, and hardware teams to design, build, and deploy perception and world‑understanding systems that directly influence how vehicles see, reason about, and interact with the world.
RESPONSIBILITIES
- Driving the technical vision and multi-year strategy for World Understanding (including perception, mapping, fusion, on-board/off-board) software.
- Leading project execution, defining efficient engineering processes, and mitigating risks.
- Collaborate with cross-functional teams such as Data, Behavior (Planner/Controller/HMI), 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 environments in various sensor configurations.
- 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 expertise in ML algorithms, frameworks, data-driven methods, supervised/unsupervised learning, multi-modality, multi-tasks training, and temporal/sequential modeling, and deployment of ML models at scale.
- 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 Planner ML system design (e.g. including supervised/unsupervised learning, reinforcement learning, diffusion policy)
- Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control)
- Japanese language skills
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