Remote Translational AI Scientist: Target Discovery

BioAge Labs; United States; Remote; USD 150,000 - 185,000; Full time

Benefits offered by this job

Remote work options

Learning budget for continued learning

Health, dental, vision benefits

401(k) matching

Job summary

BioAge Labs is seeking a scientist/engineer to build production-grade AI systems for translational science. You will design, deploy, and maintain end-to-end AI workflows that translate targets into mechanism hypotheses, experimental designs and validated readouts, grounded in retrievable evidence.

The role emphasizes hands-on biology alongside building robust AI tooling, with cross-functional collaboration to ensure study-designs are actionable and scientifically sound.

Qualifications

• PhD or relevant advanced degree with research in AI and translational science preferred.
• Experience building production-quality AI workflows with end-to-end ownership.
• Strong judgment on translational biology and data provenance.

Responsibilities

• Design and deploy agentic AI systems to support scientific reasoning and evidence synthesis.
• Develop retrieval-based workflows integrating structured and unstructured evidence with provenance.
• Evaluate translational hypotheses and design validation experiments.
• Work with target biology teams to ensure deployable, usable designs.
• Own architecture, implementation, testing, and monitoring of AI systems.
• Ensure interpretability and reliability in decision-critical settings.
• Encode rules for evidence handling and document gaps clearly.

Skills

Agentic AI systems

Evidence synthesis

Retrieval-based design

Production deployment

Multi-modal data integration

Education

PhD in CS/biology or related

MS in CS/biology

Bachelors in related field

Tools

Python

APIs

Cloud & containers

LLM tooling

Version control

Job description

BioAge Labs is seeking a scientist/engineer to build production-grade AI systems for translational science. You will design, deploy, and maintain end-to-end AI workflows that translate targets into mechanism hypotheses, experimental designs and validated readouts, grounded in retrievable evidence.

The role emphasizes hands-on biology alongside building robust AI tooling, with cross-functional collaboration to ensure study-designs are actionable and scientifically sound.