About the team:
Our work involves providing the highest performing hardware and software for deep learning, graphics, and scientific computing. We are passionate about what we do and are seeking a highly motivated, excellent performer to join our team.
What you'll be doing:
- Analyze the performance of current and next-generation GPUs for deep learning, graphics and compute workloads.
- Identify performance bottlenecks and propose solutions across the entire GPU software/hardware stack.
- Build detailed performance models and simulators to evaluate architectural trade-offs.
- Work with software and hardware teams to co-design features for optimal performance.
- Develop new performance analysis tools and methodologies.
- Investigate emerging technologies and their impact on GPU performance.
What we need to see:
- BS, MS, or PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
- Strong understanding of GPU architecture and its various sub-units (SMs, memory hierarchy, ROPs, etc.).
- Experience with performance analysis and optimization of deep learning or graphics workloads.
- Proficiency in Python and C++.
- Familiarity with CUDA programming.
- Excellent problem-solving and debugging skills.
- Strong communication and collaboration abilities.
Ways to stand out from the crowd:
- Experience with DLSS, RTX, OptiX or other NVIDIA technologies.
- Experience with ray tracing or other advanced rendering techniques.
- Knowledge of CPU architecture and its interaction with GPUs.
- Experience with workload characterization and performance modeling.
- Understanding of different GPU performance analysis tools.