Applied AI Engineer, Beneficial Deployments (Life Sciences)

Overview

In this role you will be the primary technical advisor for life sciences partners to maximize Claude’s impact from discovery to deployment. You will translate scientific workflows into scalable AI solutions and help institutions become AI-native through Claude tools. You’ll design accelerators, capture deployment challenges, and contribute to ecosystem-level improvements with engineering and product teams. This position offers meaningful collaboration with world-class research centers and a path to shape AI adoption in life sciences.

Pay / Benefits

• competitive compensation
• optional equity donation matching
• generous vacation and parental leave
• flexible working hours
• office space for collaboration

Responsibilities

• Serve as the main technical advisor to life sciences partners throughout Claude adoption
• Translate end-to-end scientific workflows into impactful solutions from discovery to deployment
• Drive partners to become AI-native using Claude Code and Claude Science enablements and process evolution
• Design and lead cohort-based accelerators to scale expertise across multiple institutions
• Identify deployment challenges and feed findings back to product, engineering, and research
• Spot patterns across partners to inform ecosystem-level developments (domain data sources, benchmarks, reusable agent skills)
• Create scalable technical content (presentations, demos, tutorials, sample code) to reduce hand-holding
• Travel to partner sites for workshops and deep dives
• Help shape team processes and culture during scale-up

Key requirements

• 8+ years in a technical role with customer-facing exposure
• Experience in life sciences, biomedical research, or scientific computing; genomics/neuroscience/drug discovery experience is a plus
• Experience working with academic or mission-driven scientific organizations
• Familiarity with LLM implementation patterns (prompt/context engineering, evaluation frameworks, agent architectures, retrieval)
• Strong teaching/mentoring skills and ability to help others succeed
• Scrappy, multi-hat mentality with ability to navigate ambiguity and advance the mission

• teaching and mentoring
• relationship-building with academic and biotech partners
• clear communication and presentation skills
• LLM implementation patterns (prompt engineering, context handling)
• evaluation frameworks
• agent architectures

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