2 hours ago
Base Salary
$280k - $320k/yr
Responsibilities
- Partner with life sciences research institutions to understand scientific workflows and build, prototype, and productionize integrated AI systems.
- Develop reusable MCP servers, domain-specific infrastructure, benchmarks, and agent skills for life sciences organizations.
- Investigate challenges in deploying AI for heterogeneous data, auditability, and trusted production workflows, and provide feedback to product, engineering, and research.
- Create technical content and documentation that enables partners to self-serve and supports global scalability.
Requirements
- Deep research experience in life sciences, biomedical research, or scientific computing, with genomics, neuroscience, or drug discovery experience preferred.
- Experience building LLM-powered tools or applications, including prompting, context engineering, agent architectures, or evaluation frameworks.
- Production software engineering, forward-deployed engineering, or technical founder experience.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field or demonstrated through relevant coursework, training, or professional experience.
- Ability to work across multiple functions, build from scratch, operate in ambiguity, and drive technical projects forward.
Benefits
- Annual base salary range of $280,000–$320,000 USD.
- Hybrid work 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 and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Categories
AI ApplicationsForward Deployed
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.