Senior High-Performance LLM Training Engineer - job 1 of 2

As a Senior High-Performance LLM Training Engineer at NVIDIA, you'll be responsible for analyzing, profiling, and optimizing AI training workloads. You'll work on innovative hardware and software platforms, implementing production-quality software for various layers of NVIDIA's deep learning platform. Engaging in solving performance problems across neural networks and supporting NVIDIA submissions to the MLPerf Training benchmark suite is also part of your role.

To qualify for the Senior High-Performance LLM Training Engineer role at NVIDIA, candidates typically need a PhD in Computer Science, Electrical Engineering, or Computer Engineering, along with 5+ years of experience. Alternatively, a Master’s degree in a related field with 8+ years of relevant experience is acceptable, complemented with a strong background in deep learning, neural networks, and GPU architecture.

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We value diversity in our workforce and do not discriminate in hiring or promotions on the basis of race, gender, or any characteristic protected by law, ensuring a welcoming and inclusive culture for all employees, including Senior High-Performance LLM Training Engineers.

Essential programming skills for the Senior High-Performance LLM Training Engineer role at NVIDIA include proficiency in C++, Python, and CUDA. These programming languages are critical for developing software solutions that optimize deep learning workloads and enhance performance on NVIDIA's GPUs.

The base salary range for a Senior High-Performance LLM Training Engineer at NVIDIA is between $184,000 and $356,500, determined by location, experience, and market pay for similar roles. Additionally, opportunities for equity and comprehensive benefits are part of the compensation package.

When answering this question, highlight specific projects where you focused on reducing training times or increasing efficiency. Discuss the tools and techniques you used, such as profiling and tuning workloads, and how your contributions impacted overall performance outcomes.

Be prepared to discuss your experience with frameworks like PyTorch or JAX in detail. Explain how you've utilized these tools in your previous work, any optimizations you've made, and the results you've achieved in training performance.

Illustrate your systematic approach to performance analysis, including profiling tools and metrics you commonly use. Discuss the importance of understanding hardware and software interactions and give examples of how you have diagnosed and solved performance bottlenecks.

Use the STAR method to structure your response. Describe the situation, the challenge you faced, the actions you took to address it, and the results. Emphasize your problem-solving skills and the importance of persistence when tackling complex neural network issues.

Discuss specific tools you've developed or improved that aid in automating workload analysis or optimization. Mention programming languages and frameworks used, and explain how these tools have enhanced workflow efficiency or performance.

Illustrate your project management skills by discussing how you assess workload performance, prioritize optimization efforts based on potential impact, and collaborate with team members to enhance overall efficiency on critical projects.

Express your enthusiasm for AI and how NVIDIA’s leadership in GPU technology and innovative culture aligns with your passion. Discuss the potential for groundbreaking advancements in AI that you hope to contribute to as part of the team.

Show your technical expertise by describing key aspects of GPU architecture you are familiar with, such as memory hierarchies and parallel processing capabilities. Relate your understanding to how it impacts performance in most deep learning applications.

Detail your involvement with benchmark testing, specifically MLPerf. Share how you prepared submissions, the metrics you focused on, and any strategies you employed to enhance performance metrics represented in the benchmarks.

Discuss your teamwork philosophy, indicating how you foster collaboration, share insights, and communicate effectively during complex projects. Provide examples where your teamwork directly influenced project success.