NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.
NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work , to amplify human creativity and intelligence. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join our diverse team and see how you can make a lasting impact on the world. As part of this team, you would be working on projects that will help make our next generation visual computing, automotive, GPU, HPC systems better. You will get to work on high performance CPU and Memory sub-systems, Next-Gen GPUs , NOC based Interconnect Fabric etc. Make the choice to join us today.
We are looking for a motivated engineer to drive the transformation of performance verification workflows through AI and automation. In this role, you will design, develop, and deploy solutions that accelerate verification cycles, improve coverage, and reduce manual effort across hardware performance verification project lifecycles. You will be at the forefront of redefining how performance verification is done at scale — replacing repetitive manual analysis with intelligent, self-improving automation that helps engineers focus on what matters most.
What you'll be doing:
Automate verification workflows by building AI/ML-based tools that generate, triage, and analyse performance test cases and results
Develop intelligent agents that can identify performance regressions, root-cause failures, and recommend corrective actions
Integrate LLM-based assistants into existing verification infrastructure to enable natural-language querying of results, specs, and coverage data
Design data pipelines to collect, curate, and label verification data for model training and continuous improvement
Collaborate with verification engineers to understand pain points, define automation priorities, and validate AI-driven solutions against real-world workflows
Establish metrics and dashboards to measure automation impact (cycle time reduction, coverage improvement, engineer productivity)
Stay current with state-of-the-art techniques in generative AI, reinforcement learning, and formal methods as they apply to hardware verification
What we need to see:
B.Tech/M.Tech/PhD in Electrical Engineering, Computer Science, or a related field
3+ years of experience in hardware verification, performance validation, or EDA tool development
Strong programming skills in Python; familiarity with C/C++, SystemVerilog/UVM is a plus
Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn) or LLM APIs (OpenAI, NVIDIA NIM/NeMo)
Understanding of performance verification methodologies (benchmarking, profiling, regression analysis)
Experience with CI/CD pipelines and infrastructure automation
Ways to stand out from the crowd:
Experience applying ML to EDA or verification problems (e.g., coverage closure, bug prediction, test generation)
Familiarity with RAG architectures, prompt engineering, and agentic AI frameworks (LangChain, CrewAI, etc.)
Knowledge of NVIDIA GPU/SoC architecture or similar complex hardware platforms
Published work or patents in AI-for-verification or related domains
#LI-Hybrid
Skills Required
- B.Tech, M.Tech, or PhD in Electrical Engineering, Computer Science, or a related field
- 3+ years of experience in hardware verification, performance validation, or EDA tool development
- Strong programming skills in Python
- Familiarity with C/C++
- Familiarity with SystemVerilog/UVM
- Hands-on experience with ML/AI frameworks such as PyTorch, TensorFlow, or scikit-learn
- Experience with LLM APIs such as OpenAI, NVIDIA NIM, or NVIDIA NeMo
- Understanding of performance verification methodologies, including benchmarking, profiling, and regression analysis
- Experience with CI/CD pipelines and infrastructure automation
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.”
