NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.
Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
At NVIDIA, we're pioneering the next step in AI: systems that do research themselves. Our team is building an autonomous, agentic platform that optimizes machine-learning models end-to-end: the model itself (architecture, hyperparameters) and its implementation (the CUDA/Triton code it compiles to) across domains. We're seeking a Machine Learning Engineer to help build the core of this platform and prove it against existing automated baselines on real model-optimization problems.
- Develop and advance a self-governing, agentic platform that optimizes AI models end-to-end — architecture, hyperparameters, and the GPU code they compile to.
- Leverage AI-native and agentic workflows to accelerate research, experimentation, evaluation, and deployment of AI systems.
- Establish and drive benchmarking frameworks that measure accuracy, latency, memory footprint, throughput, and cost — including head-to-head comparisons that prove the agent beats existing automated search.
- Design and deploy with strong consideration for reproducibility, AI safety, sandboxing, and compute-cost governance.
- Lead technical initiatives, mentor engineers, and foster a One Team culture through close collaboration across research, engineering, and product teams.
- Master's degree in Computer Science, AI, Electrical Engineering, or equivalent experience.
- 3+ years of experience building and deploying ML, LLM, or model-optimization systems.
- Strong Python skills and hands-on experience with PyTorch (or TensorFlow).
- Hands-on experience with automated experimentation — hyperparameter optimization, AutoML, or NAS.
- Experience building LLM-agent systems (reasoning, tool use, multi-step orchestration) and/or production ML pipelines and MLOps infrastructure.
- Proven technical leadership and mentoring experience, and strong problem-solving, communication, and teamwork skills.
- Hands-on experience with NVIDIA AI technologies such as NeMo, TAO, Triton, CUDA, NIM, and Nemotron.
- Experience building agentic AI systems with reasoning, tool use, and code generation.
- Expertise in optimization: evolutionary and quality-diversity search (e.g. MAP-Elites), Bayesian optimization, and multi-fidelity methods (Hyperband/ASHA).
- GPU performance work — CUDA/Triton kernels, torch.compile, operator fusion, quantization — and interest in inference-efficiency domains such as AI-RAN.
- Experience benchmarking AI systems for accuracy, latency, memory, reliability, and cost. A research track record (publications or credible reproductions) in AutoML, NAS, LLM agents, or optimization.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Skills Required
- Master's degree in Computer Science, AI, Electrical Engineering, or equivalent experience
- 3+ years building and deploying ML, LLM, or model-optimization systems
- Strong Python skills
- Hands-on experience with PyTorch or TensorFlow
- Hands-on experience with automated experimentation (hyperparameter optimization, AutoML, or NAS)
- Experience building LLM-agent systems and/or production ML pipelines and MLOps infrastructure
- Proven technical leadership and mentoring experience; strong problem-solving and communication skills
- Experience with NVIDIA AI technologies (NeMo, TAO, Triton, CUDA, NIM, Nemotron)
- Experience building agentic AI systems with reasoning, tool use, and code generation
- Expertise in optimization methods (evolutionary, MAP-Elites, Bayesian, multi-fidelity/Hyperband/ASHA)
- GPU performance work (CUDA/Triton kernels, torch.compile, operator fusion, quantization)
- Experience benchmarking AI systems for accuracy, latency, memory, reliability, and cost; research track record in AutoML, NAS, LLM agents, or optimization
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.”
