Senior Lead, Perception Onboard Robotics Software

Rivian•Palo Alto, CA

About The Position

The Perception Onboard Robotics Software Lead will direct the development of our deterministic, non-machine-learning onboard software layer. In this role, you will lead the engineering efforts responsible for the vital algorithmic software that augments our core machine learning models onboard. You will ensure this high-performance codebase provides critical safety guardrails, monitors model performance, and powers essential platform capabilities—for example, real-time safety functions or visualization displays. This position includes leading a team of individual contributors and defining the architecture for our real-time runtime environment.

Requirements

Bachelor's, Master's, or PhD in Robotics, Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical discipline.
Extensive experience developing production-grade onboard software for autonomous vehicles, robotics, or aerospace systems.
Deep theoretical and practical knowledge of classical robotics concepts—for example, spatial reasoning, state estimation, object tracking, or geometric computer vision.
Expert-level proficiency in C++ with a strong focus on real-time constraints, execution speed, memory management, and concurrency.
Proven track record of delivering deterministic, safety-critical software architectures that meet stringent latency and reliability requirements.
Experience designing software-based safety monitors, fault-detection systems, or validation guardrails for complex runtime environments.
Demonstrated experience leading, managing, or mentoring software and robotics engineering teams.
Strong cross-functional communication and coordination skills, with the ability to bridge the gap between classical robotics approaches and modern machine learning paradigms.

Responsibilities

Lead the architecture, development, and optimization of deterministic onboard perception software, drawing on core robotics disciplines such as state estimation, multi-target tracking, or sensor fusion.
Design and implement safety guardrails and runtime monitoring systems to validate and bound machine learning model outputs.
Develop highly performant software to support critical system capabilities not covered by machine learning models, for example, active safety layers or real-time human-machine interface (HMI) displays.
Enforce rigorous software engineering standards, focusing on execution speed, minimal memory footprint, deterministic behavior, and comprehensive automated testing.
Recruit, mentor, and manage a team of robotics software engineers, establishing clear technical direction and driving execution.
Collaborate closely with machine learning and systems architecture teams to seamlessly integrate classical robotics software with deep learning pipelines.