Performance Engineer, GPU
San Francisco, CA | New York City, NY | Seattle, WA
Posted 16d ago
Job Location
San Francisco, CA | New York City, NY | Seattle, WA
Tech Stack
Remote Work Policy
On-site
Categories
Applied AI Engineer
About the job
Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency. Working at the intersection of hardware and software, you'll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization. Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.
Responsibilities
- Architect and implement foundational systems for large language models.
- Maximize GPU utilization and performance at scale.
- Develop cutting-edge optimizations for model capabilities and inference efficiency.
- Implement state-of-the-art techniques from custom kernel development to distributed system architectures.
- Optimize low-level tensor core operations.
- Orchestrate thousands of GPUs for synchronized operation.
- Co-design attention mechanisms and algorithms for next-generation hardware.
- Develop custom kernels for emerging quantization formats and mixed-precision techniques.
- Design distributed communication strategies for multi-node GPU clusters.
- Optimize end-to-end training and inference pipelines for frontier language models.
- Build performance modeling frameworks to predict and optimize GPU utilization.
- Implement kernel fusion strategies to minimize memory bandwidth bottlenecks.
- Create resilient systems for planet-scale distributed training infrastructure.
- Profile and eliminate performance bottlenecks in production serving infrastructure.
- Partner with hardware vendors to influence future accelerator capabilities and software stacks.
Requirements
- Deep experience with GPU programming and optimization at scale.
- Impact-driven, passionate about delivering measurable performance breakthroughs.
- Ability to navigate complex systems from hardware interfaces to high-level ML frameworks.
- Enjoy collaborative problem-solving and pair programming.
- Desire to work on state-of-the-art language models with real-world impact.
- Care about the societal impacts of your work.
- Thrive in ambiguous environments where you define the path forward.
- Experience with GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization.
- Experience with ML Compilers & Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators.
- Experience with Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight.
- Experience with Distributed Systems: NCCL, NVLink, collective communication, model parallelism.
- Experience with Low-Precision techniques: INT8/FP8 quantization, mixed-precision techniques.
- Experience with Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience.