Used Tools & Technologies
Not specified
Required Skills & Competences
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.
Algorithms @ 4
Hiring @ 4
Git @ 7
Rust @ 7
LLM @ 4
CUDA @ 1
GPU @ 4
Deep Learning @ 4
AI @ 4
OpenCL @ 1
- 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
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. The mission is to design and develop scalable verification tools for GPU kernels that handle massive concurrency and the complex memory model of modern GPU architectures.
Responsibilities
- Design and develop robust and scalable verification tools for GPU kernels.
- Integrate verification tools into 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).
- Develop new algorithms and evaluate them; prototype ideas from research literature and contribute back where appropriate.
- Participate in architecture discussions and collaborate in hardware-software co-design efforts.
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 with source control (e.g., Git) and build systems (e.g., Make, CMake).
- Ability to work independently, define project goals and scope, and lead development efforts.
Ways to Stand Out
- 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, neurosymbolic approaches, or LLM-assisted theorem proving.
Compensation & Benefits
- Base salary range: 184,000 USD - 287,500 USD (determined based on location, experience, and pay of employees in similar positions).
- Eligible for equity and benefits (link to NVIDIA benefits provided in original posting).
Additional Information
- Applications for this job will be accepted at least until April 27, 2026.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and states nondiscrimination policies in hiring and promotion.