Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
CUDA @ 1
Deep Learning
GPU @ 4
Git @ 7
LLM @ 4
OpenCL @ 1
Rust @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
We are now looking for a Senior Formal Verification Engineer for GPU Kernels. Modern AI performance relies on highly optimized GPU kernels—performance-critical code where bugs can be hard to catch and expensive to miss. NVIDIA's Deep Learning Safety Team is hiring engineers to build the verification tools that prove these kernels behave correctly, enabling their deployment in a large range of environments, including safety-critical systems.
You will design and develop scalable verification tools for GPU kernels. You will design and implement new verification approaches that can handle the massive concurrency and complex memory model of the latest GPU architectures.
Formal methods alone cannot scale to modern GPU kernels, and AI alone cannot offer safety guarantees—the team's bet is that the combination can, and you will help build it. Join the team supporting compiler and kernel developers for safe autonomous driving.
Responsibilities
- Design and develop robust and scalable verification tools for GPU kernels.
- Integrate your work in production pipelines to support kernel and compiler developers.
- Integrate AI into formal verification workflows, build agents to automate verification tasks (formalization of specifications, bug fixing, root cause analysis).
- Participate in a high-energy and dynamic company culture to develop innovative software and hardware products and practice hardware-software co-design.
Requirements
- MS or PhD in Computer Science, Compute Engineering or equivalent experience.
- 6+ years of relevant work experience.
- Formal methods experience: symbolic execution, SMT solving, interactive theorem proving, or model checking.
- Strong programming skills in C/C++ or Rust, experience in SCM (e.g., Git) and build systems (e.g., Make, CMake).
- The ability to work independently, define project goals and scope, and lead your own development effort.
Ways to stand out from the crowd
- Knowledge of CPU and/or GPU architecture. CUDA or OpenCL experience is a plus.
- Background in the formalization of weak memory models.
- Experience in the verification of concurrent software.
- Experience building LLM agents with tool use and multi-step reasoning, or with neurosymbolic approaches and LLM-assisted theorem proving.
NVIDIA uses AI tools in its recruiting processes.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until April 27, 2026.
Location
US, CA, Santa Clara