Senior Deep Learning Performance Architect
$184,000–$356,500 year
On-site · Santa Clara, California, United States or Redmond, Washington, United States
Job Summary
Develop innovative architectures to extend the state of the art in deep learning performance and efficiency. Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites. Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications. Evaluate PPA (performance, power, area) for hardware features and system level architectural trade-offs. Develop high level simulators in C++/Python. Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW.
Required Qualifications
- MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience
- 6+ years of relevant meaningful work experience
- Strong background in GPU or Deep Learning ASIC architecture for distributed training and/or inference spanning multi-chip/multi-node
- Experience with performance modeling, architecture simulation, profiling, and analysis
- Solid foundation in machine learning and deep learning
- Understanding of modern transformer-based architectures and their performance at scale
- Strong programming skills in Python, C, C++
Desired Qualifications
- MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience
- 6+ years of relevant meaningful work experience
- Strong background in GPU or Deep Learning ASIC architecture for distributed training and/or inference spanning multi-chip/multi-node
- Experience with performance modeling, architecture simulation, profiling, and analysis
- Solid foundation in machine learning and deep learning
- Understanding of modern transformer-based architectures and their performance at scale
- Strong programming skills in Python, C, C++
- Familiarity with leading frameworks (e.g. PyTorch, JAX, TensorRT) is a plus
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