Performance Engineer, GPU

You might be a good fit if you:

• 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

Strong candidates may also 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

Logistics

Annual Salary: $280,000 — $850,000 USD

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.