Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you will architect and implement foundational systems that power Claude, maximizing GPU utilization and performance at unprecedented scale. You will develop cutting‑edge optimizations, from custom kernel development to distributed system architectures, and collaborate across hardware and software teams to orchestrate thousands of GPUs in perfect synchronization.
Performance Engineer, GPU at Anthropic
Hybrid - San Francisco, CA; New York City, NY; Seattle, WA
More jobs at AnthropicSalary
USD 280,000 - 850,000
Requirements
Skills
- Have deep experience with GPU programming and optimization at scale
- Are impact-driven, passionate about delivering measurable performance breakthroughs
- Can navigate complex systems from hardware interfaces to high-level ML frameworks
- Enjoy collaborative problem-solving and pair programming
- Want 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
- GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization
- ML Compilers & Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators
- Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight
- Distributed Systems: NCCL, NVLink, collective communication, model parallelism
- Low-Precision: INT8/FP8 quantization, mixed-precision techniques
- Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration
Responsibilities
- Architect and implement GPU performance systems powering Claude and large language models
- Maximize GPU utilization and performance at unprecedented scale
- Develop cutting‑edge optimizations that enable new model capabilities and improve inference efficiency
- Implement custom kernels, low‑level optimizations, and distributed system architectures across the entire stack
- Collaborate with hardware and software teams to orchestrate thousands of GPUs in perfect synchronization
Technologies
CUDATritonCUTLASSFlash AttentionTensor core optimizationPyTorch/JAX internalstorch.compileXLACustom operatorsNsightNCCLNVLinkCollective communicationModel parallelismINT8/FP8 quantizationMixed-precision techniquesLarge‑scale training infrastructureCluster orchestration
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