AI Accelerator, Software Principal Engineer- Graph Optimization

About the Role

In this role, you will optimize computational graphs to unlock the full potential of Ampere's deep learning accelerator. You'll work across the full SW/HW stack — from inference serving and framework integration down to compiler, runtime, and compute kernels.

Responsibilities

• Optimize deep learning computational graphs for performance, throughput, and latency on Ampere's accelerator hardware
• Enable popular models and frameworks (PyTorch, Llama.cpp) and serving platforms (vLLM, SGLang)
• Identify and implement graph-level optimizations: op fusion, pattern recognition, redundancy elimination
• Collaborate on HW/SW co-design to push computational efficiency
• Work with cross-functional teams to integrate AI solutions into Ampere's AI hardware platforms

Requirements

• Bachelor's degree in Computer Science, Mathematics or a related technical field & 8 years of related experience; or Master's degree & 6 years
• Strong CS fundamentals: algorithms, data structures, systems
• Solid graph algorithm knowledge and reasoning ability
• Proficiency in Python and C/C++
• Demonstrated exceptional problem-solving ability — IOI medal, ACM ICPC medal, Codeforces Grandmaster, USACO Platinum, or equivalent competitive programming achievement is a big plus
• Fast learner who can pick up new domains quickly and maximize agent-assisted development
• Familiarity with deep learning concepts and neural network architectures is a plus

Benefits

• Premium medical insurance, dental insurance, vision insurance
• Income protection and a 401K retirement plan
• Unlimited Flextime and 10+ paid holidays
• A variety of healthy snacks, energizing espresso, and refreshing drinks

Pay

The full base pay range for this role is between $195,000 and $292,000.