High-Performance LLM Training Engineer - New College Grad 2027
TECHNICAL STACK · 4 TAGS
OVERVIEW
GPU computing is the most productive and pervasive platform for deep learning and AI. It begins with the most advanced GPUs and the systems and software we build on top of them. We integrate and optimize every deep learning framework. We work with the major systems companies and every major cloud service provider to make GPUs available in data centers and in the cloud. We craft computers and software to bring AI to edge devices, such as self-driving cars and autonomous robots. AI has the potential to spur a wave of social progress unmatched since the industrial revolution.
We are now looking for a High-Performance LLM Training Engineer! NVIDIA is seeking experienced engineers specializing in performance analysis and optimization to improve the efficiency of LLM training workloads, which are shaping the world's most advanced computing systems. This position focuses on optimizing NVIDIA's high-performance LLM software stack in frameworks like PyTorch and JAX for high-performance pre-training and post-training on thousands of GPUs, while also helping shape hardware roadmaps for the next generation of GPUs powering the AI revolution.
WHAT YOU WILL BE DOING:
- Understand, analyze, profile, and optimize AI training workloads on innovative hardware and software platforms.
- Understand the big picture of training performance on GPUs, prioritizing and then solving problems across all state-of-the-art LLMs.
- Implement production-quality software in multiple layers of NVIDIA's deep learning platform stack.
- Implement key LLM training workloads in NVIDIA's proprietary processor and system simulators to enable future architecture studies.
- Build agentic tools to automate workload analysis, workload optimization, and other critical workflows.
WHAT WE WANT TO SEE:
- M.S. or PhD in Computer Science, Electrical Engineering or Computer Engineering (or equivalent experience).
- Strong background in deep learning and neural networks, in particular model training optimizations.
- A deep background in computer architecture and familiarity with the fundamentals of GPU architecture.
- Proven experience analyzing and tuning application performance & processor and system-level performance modeling.
- Programming skills in C++, Python, and CUDA.
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/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.QUESTIONS AND ANSWERS
- How much does the High-Performance LLM Training Engineer - New College Grad 2027 at NVIDIA pay?
- The posting lists a range of $124K–$242K per year. Ranges reflect what NVIDIA publicly declared on the source posting.
- Where is this High-Performance LLM Training Engineer - New College Grad 2027 role based?
- The role is based in US, CA, Santa Clara.
- What experience does NVIDIA expect for this role?
- The posting is tagged as a lead-level role, typically 7+ years of experience. Check the requirements section for specifics.
- How was this posting sourced?
- This role was pulled directly from NVIDIA's Workday careers site. Apply links open in the employer's own ATS — no reposts or aggregator middleware.
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