Joblaze summary

In the role of GPU Performance Engineer at Anthropic, the individual will focus on enhancing GPU performance and utilization to support advanced AI systems like Claude. This position requires expertise in GPU programming, optimization techniques, and a solid understanding of both hardware and software interactions. Ideal candidates are experienced professionals who thrive in dynamic environments and are passionate about driving impactful AI infrastructure improvements. Anthropic emphasizes collaboration and innovation, making it a fitting place for those eager to contribute to cutting-edge AI research.

Joblaze insights

Listed about 6 months ago — first seen on Joblaze April 14, 2026. Last confirmed on Anthropic's careers page October 25, 2026.
Salary band is above the typical range for AI/ML roles (median ~$190,000).
Starts above 84% of 64 comparable senior ai/ml roles in United States that list PyTorch we track (median $200,000 across 33 companies).
PyTorch appears in 18.9% of 551 comparable senior ai/ml roles in United States; XLA appears in 0.2% of 551 comparable senior ai/ml roles in United States.

Quick facts

Is the Performance Engineer, GPU role remote?: It's hybrid — Anthropic expects some on-site time in San Francisco, CA | New York City, NY | Seattle, WA.
What's the salary range?: Anthropic lists $280,000–$850,000 for this role.
Where is the role based?: Anthropic is hiring for this position in San Francisco, CA | New York City, NY | Seattle, WA.
What's the tech stack?: Joblaze extracted these technologies from the posting: CUDA, CUTLASS, FP8, Flash Attention, INT8, JAX.
Does Anthropic sponsor work visas for this role?: Yes — the posting indicates visa sponsorship is available for the right candidate.
What seniority level is this role?: Anthropic targets senior candidates for this position.
Is this full-time or contract?: Full-time for this Performance Engineer, GPU role at Anthropic.

From the original posting

About Anthropic

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.

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

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

The annual compensation range for this role is listed below.

Annual Salary:

$280,000—$850,000 USD

Logistics

Standard company text repeated across Anthropic's postings is omitted here.

Performance Engineer, GPU