Our partner is engaging advanced CUDA experts who specialize in GPU kernel optimization, performance profiling, and numerical efficiency. These professionals possess a deep mental model of how modern GPU architectures execute deep learning workloads. They are comfortable translating algorithmic concepts into finely tuned kernels that maximize throughput while maintaining correctness and reproducibility.
Requirements
- Deep expertise in CUDA programming, GPU architecture, and memory optimization.
- Proven ability to achieve quantifiable performance improvements across hardware generations.
- Proficiency with mixed precision, Tensor Core usage, and low-level numerical stability considerations.
- Familiarity with frameworks like PyTorch, TensorFlow, or Triton (not required but beneficial).
Responsibilities
- Develop, tune, and benchmark CUDA kernels for tensor and operator workloads.
- Optimize for occupancy, memory coalescing, instruction-level parallelism, and warp scheduling.
- Profile and diagnose performance bottlenecks using Nsight Systems, Nsight Compute, and comparable tools.
- Report performance metrics, analyze speedups, and propose architectural improvements.
- Collaborate asynchronously with PyTorch Operator Specialists to integrate kernels into production frameworks.
- Produce well-documented, reproducible benchmarks and performance write-ups.
Other
- Strong communication skills and independent problem-solving ability.
- Demonstrated open-source, research, or performance benchmarking contributions.
- Ideal for independent contractors who thrive in performance-critical, systems-level work.
- Work is fully remote and asynchronous; deliverables are outcome-driven.
- Structured as a contract-based engagement, not an employment relationship.
