Performance Engineer, GPU

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.

Requirements

• 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

Skills

• 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

Benefits

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

• $280,000 - $850,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy

We currently 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.

Visa sponsorship

We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time.

Our research directions

This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.

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