14 days ago
Base Salary
$350k - $850k/yr
Responsibilities
- Design and implement new information architecture strategies from scratch.
- Perform fine-tuning and reinforcement learning to teach language models to interact with information architectures.
- Build challenging knowledge-base evaluation sets to identify language-model failure modes.
- Design and evaluate advanced agentic search capabilities.
- Prototype and deliver innovative solutions with product teams.
- Develop scalable distributed information-retrieval systems and complex agentic systems using large language models.
Requirements
- Very experienced Python programming skills and the ability to produce reliable, high-quality code.
- Machine learning research experience.
- Experience developing software that uses large language models such as Claude.
- Experience building complex agentic systems using large language models is preferred.
- Experience with scalable distributed information retrieval systems, including search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing is preferred.
- Experience collaborating with product teams to prototype and deliver solutions is preferred.
- Bachelor’s degree in a relevant field or an equivalent combination of education, training, and/or experience.
- Clear written and verbal communication skills and an interest in the societal impacts of AI.
Benefits
- Competitive compensation and benefits.
- Optional equity donation matching.
- Generous vacation and parental leave.
- Flexible working hours.
- Lovely office space for collaboration.
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more.
- Visa sponsorship may be available, with immigration-lawyer support.
Tech Stack
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.