[Hiring] Translational AI Scientist/Engineer @BioAge Labs

Role Description

We are seeking a scientist/engineer with hands-on expertise in building generative and agentic AI systems and a strong foundation in target discovery, drug discovery and translational science. You will design and deploy AI-enabled systems that take a target and produce decision-grade, evidence-backed recommendations — from mechanism hypotheses to validation design — with every claim grounded in retrievable evidence and every gap stated explicitly, and that assess how likely a human-derived signal is to hold up in the lab and beyond.

This role is ideal for someone who would rather build production AI systems for translational science than only run analyses, and who has enough hands-on biology to know when a recommendation is scientifically sound and when it merely reads well. You will own the engineering of these systems end to end, from retrieval and orchestration to evaluation and deployment, and work as a peer with the scientists who act on their outputs.

What you will do

• Design and deploy agentic AI systems that support scientific reasoning, hypothesis generation and evidence synthesis for the translational questions that follow target identification: mechanism and source of signal, indication selection, experimental design, reagent quality and translatability.
• Build systems that recommend how to test a target — experimental system (in vivo, ex vivo or in vitro), model, indication, endpoints tiered by translational relevance, intervention modality and study parameters — with each choice grounded in published precedent and with an explicit statement when no precedent exists.
• Develop tool-using workflows that retrieve and integrate structured and unstructured evidence — knockout and perturbation phenotypes, endpoint precedent, published effect sizes, tool compound and reagent quality, prior programme outcomes, primary literature — with full provenance.
• Build the cross-species layer: determine whether a target's human signal is reproducible in a model system, and identify the readouts that link experimental results back to the human cohort data.
• Assess translatability: develop evidence that a human-derived signal will reproduce in vivo and onward, calibrated against targets with known preclinical and clinical outcomes, and feed that evidence back into target prioritisation.
• Build the evaluation framework for these systems — reference sets of targets with known experimental outcomes, metrics for citation quality and coverage, and calibration of translatability calls — with attention to scientific reliability and interpretability in decision-critical settings.
• Encode domain rules on the meaning and reliability of each external source, so that absent, weak and contradicting evidence are handled distinctly and never collapsed.
• Partner with target biology and experimental teams so that recommended designs are usable by the people who run the studies, and incorporate their outcomes back into the system.
• Own these systems end to end (architecture, implementation, testing, deployment and monitoring) as maintainable software that scientists rely on day to day, not one-off notebooks or prototypes.

Qualifications

• PhD with 2+ years of relevant experience, or
• Master's degree with 5+ years, or
• Bachelor's degree with 7+ years, in computer science, computational biology, biology, translation science or a related field.
• Welcoming scientist-first candidates with demonstrated ability to build production-quality AI workflows, as well as engineer-first candidates with deep translational-biology judgment.

Requirements

• Hands-on experience building and deploying LLM-based agentic systems in production or production-like settings: tool use, retrieval over structured and unstructured sources, multi-agent orchestration, structured outputs, provenance tracking, and cost and latency management.
• Experience designing and running evaluation for AI systems — reference sets, automated metrics, regression testing — with a strong understanding of interpretability and scientific reliability in decision-critical environments.
• Strong software engineering fundamentals: Python, testing, version control, API design, data modelling, and reproducible, auditable workflows (orchestration, documentation, CI).
• Experience integrating multi-modal biological data — omics, phenotype, perturbation, text and literature — using AI-enabled or model-based approaches.
• Experience with the infrastructure behind research AI systems: relational and graph databases, cloud environments and containers, pipeline orchestration, and programmatic access to large public biological databases (APIs, bulk data).

Benefits

• Competitive salary.
• Comprehensive compensation package.
• Generous paid time off in addition to company-observed holidays.
• Comprehensive health and wellness benefits (medical, dental, and vision insurance).
• 401(k) retirement savings plan with matching employer contributions.
• Support for families through childcare and fertility benefits.
• Opportunities for career development, including a generous annual budget for continued learning and a dedication to training and skill development.
• Remote work options available.
• Salary for this role is expected to be approximately $150,000 - $185,000, depending on experience.

Company Description

BioAge is a clinical-stage biopharmaceutical company developing therapeutic product candidates for metabolic diseases by targeting the biology of human aging. The Company's lead product candidate, BGE-102, is a potent, orally available, brain-penetrant small-molecule NLRP3 inhibitor being developed for cardiovascular risk and retinal diseases including diabetic macular edema. BGE-102 has completed a Phase 1 SAD/MAD trial demonstrating a well-tolerated profile and potential best-in-class reductions in hsCRP and other inflammatory biomarkers in participants with obesity and elevated inflammation. Phase 2 cardiovascular risk proof-of-concept data are anticipated in H2 2026, and Phase 1b/2a diabetic macular edema proof-of-concept data are anticipated in mid 2027. The Company is also developing long-acting injectable and oral small molecule APJ agonists for obesity. BioAge’s additional preclinical programs, which leverage insights from the Company’s proprietary discovery platform built on human longevity data, address key pathways involved in metabolic aging. BioAge has been listed on Nasdaq (BIOA) since 2024. For additional information about BioAge, visit the company’s website at bioagelabs.com .