1 year ago
Base Salary
$315k - $560k/yr
Responsibilities
- Architect and implement foundational GPU performance and systems infrastructure for Claude and large language models.
- Develop custom kernels, tensor-core optimizations, kernel fusion strategies, and quantization techniques.
- Optimize end-to-end training, inference, and production serving pipelines.
- Design distributed communication strategies and orchestrate large multi-node GPU clusters.
- Build performance modeling frameworks and profile and eliminate production bottlenecks.
- Create resilient systems for large-scale distributed training and partner with hardware vendors on future accelerator capabilities and software stacks.
Requirements
- Deep experience with GPU programming and optimization at scale.
- A track record of delivering transformative GPU performance improvements in production ML systems.
- Ability to navigate systems from hardware interfaces through high-level ML frameworks.
- Experience with GPU kernel development, ML compilers and frameworks, performance engineering, distributed systems, low-precision techniques, or production-scale training infrastructure is valued.
- Bachelor's degree in a related field or equivalent experience.
- Strong collaboration, communication, problem-solving, and ability to work effectively in ambiguous environments.
Benefits
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship may be available, with immigration-lawyer support.
- Competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
About Anthropic
Anthropic builds large language models and the Claude AI assistant for developers and enterprises, offered via API access and enterprise plans. Founded in 2021 and headquartered in San Francisco, it distributes Claude through its own platform and via partners such as Amazon Bedrock and Google Cloud’s Vertex AI. Its work emphasizes model reliability, interpretability, and practical tooling for tasks like coding assistance, analysis, and customer support automation.