What you’ll be doing:
Develop innovative architectures to extend the state of the art in deep learning performance and efficiency
Prototype key deep learning algorithms and applications
Analyze performance, cost and energy trade-offs by developing analytical models, simulators and test suites
Characterize power and performance on silicon parts and translate the learnings to architecture features and simulators.
Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications
Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW
What we need to see:
A Masters Degree (or equivalent experience) and 6+ years of meaningful relevant experience or PhD and 3+ years of experience in Computer Science, Electrical Engineering, Computer Engineering, or related field
Strong foundation in Deep Learning model architectures and performance tradeoffs
Experience with performance and energy modeling, power architecture, architecture simulation, profiling, analysis, and visualizations
Strong programming skills in Python, C, C++
Experience with the architecture of, or workload analysis on, DL accelerators
Ways to stand out from the crowd:
Background with GPU Computing and parallel programming models such as CUDA
Experience with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, JAX)
You will also be eligible for equity and .
משרות נוספות שיכולות לעניין אותך