Senior System Software Engineer, Robotics

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Employment: Full-time
Posted: 4 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $206k

This role $197k

$140k most similar roles pay here $265k

This role pays less than 54% of similar roles. Most pay $177,250–$235,750 — the shaded band above. At the midpoint, this role pays about $197k versus about $206k for comparable roles.

Based on 240 similar postings.

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 892 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 870 roles with salary data.

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At a glance

TL;DR · Senior System Software Engineer, Robotics

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As a Senior System Software Engineer, Robotics, you will join the Robotics Team to develop system software, runtime components, tools, and workflows that enable the reliable deployment of embodied AI models on humanoid robots. You will be responsible for optimizing model execution across compute, memory, networking, sensors, and actuators while building validation, testing, benchmarking, and observability infrastructure across simulation and hardware-in-the-loop environments. Your daily work involves debugging complex issues across middleware, operating systems, device drivers, and firmware to improve metrics like latency, determinism, and throughput. You will utilize Linux, embedded software, and distributed computing to integrate foundation models into production robotic systems. The role focuses on the technical challenge of integrating learning-based policies into physical hardware, ensuring high standards for system performance, maintainability, and operational readiness in the field of humanoid robotics.

Skills

Linux Embedded Software Real-time Systems Distributed Computing Hardware-in-the-loop (HIL) System Software Firmware Device Drivers Robotics Simulation Performance Optimization Sensor Integration

What you'll do

Develop system software, runtime components, and workflows to deploy embodied AI models on physical robots.
Optimize model execution and performance across compute, memory, networking, sensors, and actuators.
Build validation, testing, benchmarking, and observability infrastructure for simulation and hardware-in-the-loop environments.
Measure and improve system metrics including end-to-end latency, determinism, throughput, and resource utilization.
Debug complex issues across application software, middleware, operating systems, device drivers, firmware, and hardware.
Bring up, validate, and harden robotic systems for real-world deployment in collaboration with cross-functional teams.
Perform root-cause analysis and investigation to resolve technical issues through field validation.
Contribute to architectural decisions and maintain high standards for system software quality and maintainability.

What we're looking for

BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, Robotics, or a related field (or equivalent experience).
5+ years of experience developing robotics, embedded, platform, distributed, or system software.
Strong understanding of robotics system architecture, platform software, and hardware-software integration.
Hands-on experience bringing up, debugging, and deploying complex robotic systems on physical hardware and in simulation.
Strong knowledge of Linux, embedded software, distributed computing, and real-time systems.
Proven ability to provide technical leadership and deliver complex projects across multiple functions.
Experience building and shipping humanoid robotic systems or reference platforms (preferred).
Background with deploying foundation models, embodied AI models, or reinforcement learning policies on physical robots (preferred).