This role is focused on motion planning, trajectory generation, and trajectory optimization for autonomous vehicles in unstructured environments. You will provide technical leadership across teams and platforms, driving architectural strategy, engineering excellence, and scalable system design. You will lead high-impact technical initiatives, influence long-term engineering direction, and contribute directly to the design, development, validation, and deployment of planning capabilities.
This role operates at a multi-team level with broad organizational impact. You will work closely with engineers and leaders across autonomy, controls, simulation, safety, and validation to help deliver safe, robust, and reliable autonomy behaviors at scale.
Technical Leadership
Defining and maintaining the architectural vision across teams, platforms, and services to ensure scalability, reliability, maintainability, long-term technical alignment, and consistency of software design patterns
Supporting Engineering Managers and Senior ICs in resolving complex technical design, architecture, and implementation challenges across multiple domains while balancing feasibility against business objectives
Establishing and promoting engineering best practices for code quality, automated testing, performance optimization, monitoring, security, and operational excellence
Conducting architecture reviews, technical deep dives, design sessions, and technical assessments to guide engineering decisions and solution direction
Mentoring and coaching senior and mid-level engineers while helping grow future Staff and Principal engineers through technical guidance and leadership development
Fostering a culture of engineering excellence, documentation, collaboration, innovation, and knowledge sharing across the organization
Translating complex technical concepts, trade-offs, and architectural decisions into clear recommendations for leadership and cross-functional stakeholders
Leading major technical initiatives such as platform modernization, observability enhancements, performance optimization, scalability improvements, and reliability engineering efforts
Software Development
Qualification
Bachelor's degree in Computer ScienceProven experience operating as a StaffDeep expertise developing roboticsStrong foundation in linear algebraExperience with version control (Git)Exposure to CUDAPassion for autonomous vehicles
Required
Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field with: 10+ years of industry experience OR Master's degree with 7+ years of experience OR PhD with 5+ years of experience.
Proven experience operating as a Staff, Principal, or multi-team technical leader within complex engineering organizations including experience leading large cross-functional technical initiatives across multiple engineering teams
Deep expertise developing robotics, autonomous vehicle, ADAS, or other complex real-time software systems.
Deep expertise in modern C++ development within Linux-based environments.
Diverse experience developing motion planning, trajectory generation, trajectory optimization, behavior planning, or decision-making systems for robotics or autonomous systems.
Strong understanding of vehicle dynamics, vehicle kinematics, trajectory feasibility, and planning system architecture.
Strong foundation in linear algebra, numerical optimization, geometry, and robotics algorithms.
Hands-on experience with analyzing metrics to optimize performance in a resource-constrained environment.
Excellent troubleshooting skills with the ability to perform both real-time diagnostics and in-depth post-analysis.
Mastery of software engineering fundamentals, system design principles, and scalable development practices.
Experience with version control (Git), build systems, and package management.
Understanding of test automation principles and proficiency with unit testing and test automation frameworks (pytest, Robot Framework, gtest, etc.)
Preferred
Experience with Model Predictive Path Integral (MPPI), Model Predictive Control (MPC), convex optimization, quadratic programming (QP), nonlinear optimization, or other optimization-based planning techniques for autonomous vehicles.
Experience applying machine learning techniques such as imitation learning, reinforcement learning, or hybrid planning architectures.
Experience integrating machine learning models into real-time or production systems.
Experience working with simulation platforms and large-scale autonomy validation environments.
Experience with robotics frameworks such as ROS/ROS2, Autoware, Apollo, or similar autonomy platforms.
Exposure to CUDA, GPU acceleration, TensorRT, or high-performance compute environments.
Experience supporting on-vehicle testing, debugging, and deployment activities.
Passion for autonomous vehicles, robotics, and solving complex real-world engineering challenges.
