Senior Software Engineer, Planning and Control

NVIDIA•Santa Clara, CA

About The Position

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are seeking a Planning & Control Engineer to build and ship motion planning and vehicle control capabilities for autonomous driving and advanced driver assistance systems. You will own core algorithms and their production integration—turning complex, uncertain real-world inputs into safe, comfortable, and efficient vehicle behavior across diverse scenarios. This role spans end-to-end autonomy from planning logic and optimization through control, calibration, testing, and performance validation in simulation and on-vehicle.

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
Strong C++ development skills and experience shipping performance-sensitive systems.
Proven foundation in robotics and controls (kinematics/dynamics, estimation basics, feedback control).
Experience with planning techniques and constraints-based decision making under uncertainty.
Ability to design tests, metrics, and evaluation methodology; comfort with debugging real-world edge cases.

Nice To Haves

Experience with production ADAS/AV planning and/or control stacks.
Experience with trajectory optimization, MPC, iLQR, sampling-based planning, search-based planning, or hybrid approaches.
Familiarity with automotive interfaces, vehicle dynamics, and calibration/validation workflows.
Experience with large-scale simulation, scenario generation/mining, and log-based evaluation.
Python tooling for analysis, visualization, and offline evaluation pipelines.

Responsibilities

Design and implement behavior and motion planning systems (lane/route following, merges, unprotected turns, interactions, yielding, etc.).
Develop trajectory generation methods (sampling/search, optimization-based, MPC-style, rule/constraint-based) that satisfy safety, comfort, and traffic constraints.
Integrate prediction, map, and localization inputs; handle uncertainty and imperfect/partial observations.
Define and enforce constraints (collision avoidance, kinematic/dynamic feasibility, traffic rules, passenger comfort).
Build longitudinal and lateral control algorithms (tracking, stabilization, speed control, steering control) for robust performance across conditions.
Develop controllers resilient to latency, actuator limits, disturbances, and changing vehicle dynamics.
Implement fallback behaviors and runtime checks (trajectory validity, constraint violations, system health).
Establish metrics and evaluation pipelines: safety proxies, comfort, rule compliance, progress, and robustness.
Debug complex autonomy failures using logs, visualization, replay, and scenario triage.
Improve performance and reliability for real-time systems (latency budgets, determinism, profiling).
Participate in architecture reviews; define clean APIs and component contracts.