Overview As a Senior GPU & Deep Learning Architect, you will shape the next generation of GPU architectures for training and inference on deep learning workloads. You'll work with world-class researchers to build simulators, map DL workloads to hardware, and validate architectural features. You'll design new hardware capabilities, advance parallel computation, and develop tooling and tests for verification. This role offers impact on cutting-edge AI platforms and collaboration across a top-tier team.
Compensation / Benefits- equity
- benefits
- base salary with level-based ranges
- career growth opportunities
Responsibilities- Design new hardware features for future processing architectures targeted at deep learning workloads (training and inference)
- Advance parallel computation and stay ahead of future parallel programming models and their hardware impact
- Develop software for hardware simulators, test infrastructures, and metrics systems including databases
- Collaborate to document, design, develop tools to analyze/simulate, validate, and verify models
- Develop tests, test plans, and testing infrastructure for new graphics or parallel processing architectures
- Learn and work on simulators, RTL and real silicon
Key requirements- MS in Computer Science, Electrical Engineering or Computer Engineering or equivalent experience
- Experience with hardware targeted at deep learning or mapping DL to hardware
- 8+ years in GPU or parallel programming architectures or equivalent
- Strong programming ability in C, C++, Perl and Python
- Background in computer architecture, parallel processing, signal processing and/or high performance computing
- Knowledge of state-of-the-art DL algorithms and attention mechanisms (a plus)
- hungry to learn and work on simulators, RTL and real silicon
- creative and autonomous
- team-oriented, collaborative
- C, C++, Perl, Python
- hardware simulators and test infrastructures
- RTL and silicon verification
