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.
What You'll Be Doing
- 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.
What We Need To See
- 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.
Ways To Stand Out From The Crowd
- 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/
, , JR2021274
