Create engineering solutions to deliver continuous insights into performance of AI workloads guiding the improvements over time.
Design and implement high-level kernels, e.g. in Triton, with a focus on generating efficient, low-level code.
Candidates for this position will demonstrate the following
Hands-on experience in building and optimizing language models, notably in PyTorch, ONNX, preferably in production-grade environments.
Deep understanding of transformer architectures, attention mechanisms and performance trade-offs.
Experience in workload mapping strategies exhibiting sharding or various parallelisms.
Strong Python programming skills.
Proactive learning about the latest inference optimization techniques.
Understanding of computer architecture, ML accelerators, in-memory processing and distributed systems.
Strong communication, problem-solving skills and ability to learn and work effectively in a fast-paced and collaborative environment.
MS in Computer Science, Machine Learning, Computer Engineering or Electrical Engineering.
Bonus Skills
Background in neural network operators and mathematical operations, including linear algebra and math libraries.
Understanding of machine learning compilers.
Experience in converging accuracy and its evaluation methods.
Knowledge of torch.compile or torchDynamo.
PhD in Computer Science, Computer Engineering or Machine Learning
Minimum Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- OR
- Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- OR
- PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
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