Applied AI Engineer, Beneficial Deployments
Build and deploy LLM-powered tools, agents, and ecosystem infrastructure with life sciences research institutions. The role requires deep scientific or biomedical research experience, production software development expertise, and the ability to translate partner workflows into scalable AI systems.
About the job
Responsibilities
- Partner with flagship life sciences research institutions to understand scientific workflows, build with engineering teams, and take projects from exploration to production.
- Develop reusable ecosystem infrastructure, including MCP servers for domain-specific data sources, instruments, scientifically grounded benchmarks, and agent skills.
- Identify challenges in deploying AI in life sciences, including heterogeneous data, auditability, and the prototype-to-trust gap, and share findings with product, engineering, and research teams.
- Create technical content and documentation that enables partners to self-serve and scales successful solutions across institutions.
Requirements
- Deep research experience in life sciences, biomedical research, or scientific computing.
- Experience with genomics, neuroscience, or drug discovery is a plus.
- Experience building LLM-powered tools or applications, including prompting, context engineering, agent architectures, or evaluation frameworks.
- Production software engineering experience as a software engineer, forward-deployed engineer, or technical founder.
- Ability to work across multiple responsibilities, build from scratch, and operate effectively in ambiguous situations.
- Bachelor's degree or equivalent combination of education, training, and experience in a relevant field.
Compensation and Benefits
- Annual salary: $280,000–$320,000 USD.
- Competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Skills
Life Sciences, Biomedical Research, Scientific Computing, Genomics, Neuroscience, Drug Discovery, LLMs, Prompting, Context Engineering, Agent Architectures, Evaluation Frameworks, Mcp Servers, Python, Software Engineering
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