Join NVIDIA's Isaac Applications Engineering team and help build the platform for Physical AI robots — assembling the full robotics stack and proving it out on real hardware, from simulation through deployment. This role owns a question that runs through all of it: is our software ready to be used by agents? That means the coding agents we build with, the agents our customers will use to build on our stack, and the embedded agents running on the robot itself. You'll design for it, build for it, test for it, and build the infrastructure that keeps it true as the stack evolves.
What you'll be doing:
Design for it. Shape APIs, error surfaces, tooling, and documentation so that agents can build with our robotics stack as effectively as human engineers — across multiple products and neighboring teams.
Build for it. Create agent skills and automated workflows that teams adopt in their own repos, and support embedded agents on the robot (code-as-policy and related approaches) where latency, safety, and determinism constraints apply.
Test for it. Run and integrate skill evaluations so agentic workflows are measured, not just deployed, and turn those evaluations into automated checks that catch regressions before they land.
Build the infrastructure that keeps it true. Own CI/CD across the applications repos — spanning CMake, Bazel, and Python builds — including runners, Docker images, dataset provisioning, nightly builds, and release and docs pipelines.
Spread what works. Package patterns into docs, templates, and starter kits, then drive adoption — running the conversations with teams and following through until a practice is live in more than one repo.
What we need to see:
Master's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience).
5+ years of professional software engineering experience.
Fluency across the kinds of software a robotics stack is made of — policy training, simulation, on-robot deployment — and the judgment to work effectively in whichever language and toolchain each of those calls for.
Hands-on experience building and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, or similar), with proven Docker and Linux fundamentals.
Practical experience using coding agents or LLM-based developer tools in real work, and a point of view on where they succeed and where they fall over.
A bias toward making other engineers faster, and the follow-through to get a practice embraced rather than just proposed.
Clear written communication — a large part of this role is documentation, templates, and persuading teams to change how they work.
Ways to stand out from the crowd:
Experience building agent skills, tool integrations, or MCP servers, and evaluating agent performance systematically.
Familiarity with NVIDIA robotics products such as Jetson, Isaac Sim/Lab, or Isaac ROS.
Background in robotics, embedded systems, or another domain where software has to run on real hardware.
Experience managing GPU-backed CI infrastructure or self-hosted runner fleets.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Master's degree in Computer Science, Robotics, Engineering, or related field (or equivalent experience).
- 5+ years of professional software engineering experience.
- Fluency across robotics software domains (policy training, simulation, on-robot deployment) and adaptable judgement across languages/toolchains.
- Hands-on experience building and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, or similar).
- Proven Docker and Linux fundamentals.
- Practical experience using coding agents or LLM-based developer tools in real work and informed point of view on their limitations.
- Strong written communication, documentation, and ability to drive cross-team adoption of practices.
- Bias toward improving other engineers' productivity and follow-through to implement practices.
- Experience building agent skills, tool integrations, or MCP servers.
- Familiarity with NVIDIA robotics products (Jetson, Isaac Sim/Lab, Isaac ROS).
- Background in robotics, embedded systems, or software that runs on real hardware.
- Experience managing GPU-backed CI infrastructure or self-hosted runner fleets.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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