Qualcomm is investing in Deep Learning and developing hardware and software solutions for Inference Acceleration for Cloud AI, and is looking for an AI Performance Engineer to contribute to this effort.
Requirements
- 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.
- Background in neural network operators and mathematical operations, including linear algebra and math libraries.
Responsibilities
- Convert, optimize and deploy models for efficient inference using PyTorch, ONNX.
- Work at the forefront of GenAI by understanding advanced algorithms (e.g. attention mechanisms, MoEs) and numerics to identify new optimization opportunities.
- Performance analysis and optimization of LLM, VLM, and diffusion models for inference. Scale performance for throughput and latency constraints.
- Mapping the next generation AI workloads on top of current and future hardware designs.
- Work closely with customers to drive solutions by collaborating with internal compiler, firmware and platform teams.
- Analyze complex performance or stability issues to work towards final root cause of underlying problems.
- Create engineering solutions to deliver continuous insights into performance of AI workloads guiding the improvements over time.
Other
- Strategic thinking, strong execution, and excellent communication skills.
- 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.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- Master's degree in Computer Science, Engineering, Information Systems, or related field and 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
