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Performance Engineer, GPU
Description
About the role:
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.
Qualifications
- 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
Nice to have
- 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
- 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
Benefits
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