Translational AI Scientist/Engineer

BioAge Labs

4d • $150,000 - $185,000 • Remote

About The Position

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.

Requirements

• 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.
• Demonstrated ability to build production-quality AI workflows.
• Deep translational-biology judgment.
• 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).
• Hands-on or wet-lab research experience in one or more of: in vivo pharmacology or disease models, target validation, disease biology, functional genomics, chemical biology or translational science — enough to judge whether a proposed experiment or mechanism is sound.
• Working knowledge of the resources that hold target and precedent evidence — model organism phenotype databases, chemical probe and druggability resources, drug and clinical trial databases, pathway and perturbation resources — and the judgment to know when each is reliable.
• Familiarity with the evidence linking preclinical results to clinical outcomes, including the role of genetic support and the literature on animal-to-human translation.
• Scientific rigour about evidence: you distinguish absence of evidence from evidence against, report coverage alongside conclusions, and prefer stating a gap to filling it plausibly.
• Ability to work effectively across scientific and engineering functions, with strong written and oral communication, self-motivation and independence.

Nice To Haves

• Background in aging biology or geroscience, including healthspan endpoints and aging-specific study designs.
• Experience with proteomics, cross-species biomarker translation or biomedical ontologies.
• Familiarity with CRISPR and perturbation resources, or with pooled or arrayed screening.

Responsibilities

• 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.

Benefits

• Competitive salary
• Comprehensive compensation package
• Generous paid time off
• Company-observed holidays
• Comprehensive health and wellness benefits (medical, dental, and vision insurance)
• 401(k) retirement savings plan with matching employer contributions
• Childcare benefits
• Fertility benefits
• Opportunities for career development
• Generous annual budget for continued learning
• Dedication to training and skill development

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree