At Gatik, we connect people of extraordinary talent and experience to an opportunity to create a more resilient supply chain and contribute to our environment’s sustainability.
Key Responsibilities
We are seeking senior or staff software engineers to join our planning team to build motion planning and decision-making systems and help mature new products all the way through to production. Our planning team calculates safe paths for the autonomous vehicle to follow using mapping data, localization data, waypoints, and predicted actors around the vehicle. Your contribution will improve planning for emergency situations and planning for complicated maneuvers such as loading dock access and variable lane changes.
This role is onsite 5 days a week at our Santa Clara, CA office!
Design, implement, and optimize the motion planning stack for autonomous vehicles, enabling safe, comfortable, and efficient driving in diverse real-world environments.
Develop lattice-based planners, sampling-based planners, and optimization-based methods for generating dynamically feasible, collision-free trajectories.
Integrate constraints from perception, prediction, and control to generate context-aware motion plans under uncertainty.
Collaborate cross-functionally with behavior planning, control, and mapping teams to ensure robust and consistent planning behavior.
Optimize and maintain C++-based infrastructure for trajectory generation, evaluation, and deployment in real-time onboard systems.
Analyze closed-loop simulation and on-road performance; identify and resolve failure cases involving traffic interactions, occlusions, or dynamic obstacles.
Requirements
Ph.D. with 3+ years or Master’s with 5+ years of experience in robotics, motion planning, or autonomous systems.
Deep expertise in at least two of the following: lattice-based planning, trajectory optimization, model predictive control, or search-based planning.
Strong proficiency in C++, with experience in production-quality code for real-time or safety-critical systems.
Solid understanding of vehicle dynamics, control interfaces, and motion feasibility constraints.
Experience with simulation tools and debugging motion planner behavior in realistic driving environments.
Knowledge of diffusion models and/or Vision-Language-Action (VLA) models is a strong plus.
Excellent analytical and system-level debugging skills; strong communication and cross-functional collaboration capabilities.