Bachelor’s degree or equivalent practical experience.
8 years of experience in software development and with data structures/algorithms (e.g., C++ or Python).
5 years of experience with Machine Learning (ML) design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience working with GPUs.
Experience in a technical leadership role leading project teams and setting technical direction.
Preferred qualifications:
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).
Expertise in tailoring algorithms and ML models to exploit GPU strengths and minimize weaknesses.
Knowledge of low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques.
Understanding of modern GPU architectures, memory hierarchies, and performance bottlenecks.
Ability to develop and utilize sophisticated performance models and benchmarks to guide optimization efforts and hardware roadmap decisions.