Description
As a GPU Performance Engineer at Anthropic, you will be responsible for architecting and implementing the foundational systems that power Claude and push the frontiers of what's possible with large language models. You will maximize GPU utilization and performance at unprecedented scale, develop cutting-edge optimizations that directly enable new model capabilities, and dramatically improve inference efficiency.
Working at the intersection of hardware and software, you will 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 that power Claude
- Maximize GPU utilization and performance at unprecedented scale
- Develop cutting-edge optimizations that directly enable new model capabilities
- Dramatically improve inference efficiency
- Implement state-of-the-art techniques from custom kernel development to distributed system architectures
- Work at the intersection of hardware and software
- Span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization
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
- 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
Nice to have:
- Experience with 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
Representative projects:
- Co-design attention mechanisms and algorithms for next-generation hardware architectures
- 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
Note: The salary range for this position is $280,000-$850,000 USD per year.