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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Performance Engineer - **Company:** Anthropic Limited - **Location:** San Francisco, CA, United States - **Salary:** $280,000.0 - **Contract:** Permanent contract - **Skills:** Adobe Flash, Java (Programming Language), Artificial Intelligence, Systems Engineering, Compilers, Nvidia CUDA, Computer Programming, Distributed Systems, Fault Tolerance, Hardware Interface Design, Linux Kernel, Language Modeling, Pair Programming, Tensorflow, Systems Architecture, Extensible Markup Language (XML), AI Infrastructure, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Gpu Programming, Information Technology, Machine Learning Operations - **Published:** July 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/performance-engineer-gpu-san-francisco-ca--afaf62f6-e361-42f0-a79c-0a6d043a72e5 ## About the Role * 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, 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: 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., Adobe Flash, Algorithms, Alliance/Partner Marketing, Artificial Intelligence (AI), Banking Services, Biology, CUDA (Compute Unified Device Architecture), Communication Skills, Computer Science, Concrete, Distributed Computing, Frontier Programming Language, GPU (Graphics Processing Unit), Hardware Architecture, JAX (Java API for XML), Kernel Programming, Large-Scale Systems, Memory Hardware, Modeling Languages, Performance Engineering, Performance Management, Performance Metrics, Performance Modeling, Physics, Predictive Modeling, Problem Solving Skills, Production Systems, Recruiting/Staffing Agency, System Architecture, Systems Engineering, Team Player ## Description 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. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Just-in-time Compilation in JVM](https://www.wearedevelopers.com/videos/240-just-in-time-compilation-in-jvm) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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