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
Develop, analyze, and harness groundbreaking Deep Learning frameworks, libraries, and compilers
Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW

Requirements

MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience
Strong background in GPU or Deep Learning ASIC architecture for training and/or inference
Experience with performance modeling, architecture simulation, profiling, and analysis
Solid foundation in machine learning and deep learning
Strong programming skills in Python, C, C++

Benefits

equity
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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

deep learningGPU architectureASIC architectureperformance modelingarchitecture simulationprofilingPythonCC++

Soft Skills

collaborationanalytical skills

Certifications

MS in Computer SciencePhD in Computer ScienceMS in Computer EngineeringPhD in Computer EngineeringMS in Electrical EngineeringPhD in Electrical Engineering