2 months ago
Base Salary
$350k - $500k/yr
Responsibilities
- Build large-scale ML systems from the ground up.
- Improve cluster reliability, throughput, computational efficiency, and developer tooling.
- Run and design scientific experiments in the context of machine learning research.
- Optimize attention mechanisms and compare the compute efficiency of Transformer variants.
- Prepare datasets for model consumption and scale distributed training jobs to thousands of GPUs.
- Design fault-tolerance strategies and create visualizations of attention in language models.
Requirements
- Significant software engineering experience.
- A bachelor's degree or an equivalent combination of education, training, and/or experience.
- A field of study relevant to the role as demonstrated through coursework, training, or professional experience.
- Strong candidates may have experience with high-performance large-scale ML systems, GPUs, Kubernetes, PyTorch, OS internals, language modeling with Transformers, reinforcement learning, or large-scale ETL.
- Candidates should be results-oriented, flexible, collaborative, willing to take on work beyond their formal job description, and interested in machine learning research and its societal impacts.
Benefits
- Annual salary range of $350,000-$500,000 USD.
- Hybrid work policy requiring staff to be in an Anthropic office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship may be available, with immigration lawyer support.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Tech Stack
Categories
AI ResearchML Engineering
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.