Location
Santa Clara, CA, US
Salary
(Yearly)
(Yearly)
(Yearly)
(Yearly)
(Hourly)
Undisclosed
$130,000 – $220,000
Category
Robotics Engineer
Date posted
August 28, 2026
Job type
Full-time
Experience level
Summary this job with AI
PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.
About the TeamThe Planning team is responsible for making sure PlusAI’s trucks make the right decision in every driving situation, safely, smoothly, and predictably for other road users, even when working with noisy, uncertain, or incomplete information about the world. This is a cross-functional problem that requires close collaboration with Perception, Prediction, Controls, and Safety, and a willingness to work at the edge of research and production: bringing state-of-the-art decision-making and optimization techniques into a system that runs on real trucks, on real roads, today.
About the RoleAs a Senior Software Engineer on the Motion Planning team, you’ll research, develop, and implement planning technologies that let PlusAI’s trucks handle complex, real-world driving scenarios in a safe and human-like manner. You’ll work across the full decision-making stack, from reasoning about what the truck should do to determining precisely how it should get there, while accounting for the many ways a scenario could unfold.
About the RoleAs a Senior Software Engineer on the Motion Planning team, you’ll research, develop, and implement planning technologies that let PlusAI’s trucks handle complex, real-world driving scenarios in a safe and human-like manner. You’ll work across the full decision-making stack, from reasoning about what the truck should do to determining precisely how it should get there, while accounting for the many ways a scenario could unfold.
Responsibilities
- Design and implement algorithms for decision-making and trajectory generation, ensuring safety, comfort, and reliability across a wide range of driving conditions.
- Bring novel robotics and machine learning techniques from research into a production-grade, safety-critical system.
- Reason about complex, socially-nuanced driving situations, such as negotiating merges, junctions, and interactions with other road users, under uncertainty.
- Utilize modern C++ numerical optimization frameworks (e.g., CasADi, Acados) and industry-standard tooling (git, ROS, etc.) to integrate new planning algorithms into the production stack.
- Partner closely with the ML and prediction teams to integrate, refine, and act on probabilistic and multi-future representations of how a scenario might evolve, shaping how these outputs are consumed by planning decisions.
- Perform system-level diagnostics and tuning using in-house and open-source visualization tools (e.g., webviz).
- Collaborate cross-functionally and communicate clearly, producing system designs that are reviewed and approved at a high level before implementation begins.
Required skills
- MS with 1-2 years of relevant experience, or PhD, in Computer Science, Robotics, or a related field.
- Strong production software development skills in C++; Python experience a plus.
- Robotics expertise in one or more of: path planning, motion planning, trajectory generation, behavior planning, optimal control.
- Strong math (geometry, analysis, probability) and physics (kinematics, dynamics) fundamentals.
- Experience designing, implementing, and refining planning algorithms on a real-world robotic or vehicle platform.
- Strong oral and written communication skills, with the ability to collaborate across teams and derive solutions from cross-functional input
Preferred skills
- PhD + 1-2 years or MS + 3-4 years of relevant experience.
- Bonus: exposure to machine learning techniques such as deep learning, imitation learning, transformer-based models, or diffusion-based models, particularly as applied to decision-making or scenario generation.
- Familiarity with safety frameworks for autonomy (e.g., RSS, collision avoidance/MRM logic).
- Prior experience working on safety-critical systems.
