Is the Translational AI Scientist/Engineer position at BioAge Labs fully remote?

Yes, this Translational AI Scientist/Engineer position at BioAge Labs is a remote position. The role is available for candidates in United States.

Is this a full-time or part-time Translational AI Scientist/Engineer role?

This is a full-time Translational AI Scientist/Engineer position at BioAge Labs.

What benefits does BioAge Labs offer for this position?

BioAge Labs offers the following benefits: Health Insurance, Dental Insurance, Vision Insurance, Retirement Plan, Paid Time Off.

What is the salary range for the Translational AI Scientist/Engineer position?

The salary range for this Translational AI Scientist/Engineer role is $150k - $185k.

How do I apply for the Translational AI Scientist/Engineer position at BioAge Labs?

You can apply for this Translational AI Scientist/Engineer position by clicking the "Apply Now" button on this page, which will take you to BioAge Labs's application portal.

Translational AI Scientist/Engineer at BioAge Labs

United States

$150k - $185k

<div><body>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.<h2 style='font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-size:29pt;font-weight:600;letter-spacing:0.5px;margin-top:18pt;margin-bottom:4pt;padding-left:0px;'>What you will do</h2><ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li></ul><h2 style='font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-size:29pt;font-weight:600;letter-spacing:0.5px;margin-top:18pt;margin-bottom:4pt;padding-left:0px;'>What we expect of you</h2>Basic qualifications<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:12pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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. We welcome scientist-first candidates with demonstrated ability to build production-quality AI workflows, as well as engineer-first candidates with deep translational-biology judgment.</li></ul>Preferred technical qualifications<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Strong software engineering fundamentals: Python, testing, version control, API design, data modelling, and reproducible, auditable workflows (orchestration, documentation, CI).</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Experience integrating multi-modal biological data — omics, phenotype, perturbation, text and literature — using AI-enabled or model-based approaches.</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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).</li></ul>Preferred scientific qualifications<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Familiarity with the evidence linking preclinical results to clinical outcomes, including the role of genetic support and the literature on animal-to-human translation.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">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.</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Ability to work effectively across scientific and engineering functions, with strong written and oral communication, self-motivation and independence.</li></ul>Also valued<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Background in aging biology or geroscience, including healthspan endpoints and aging-specific study designs.</li><li style="color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Experience with proteomics, cross-species biomarker translation or biomedical ontologies.</li><li style="color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;letter-spacing:0.25px;">Familiarity with CRISPR and perturbation resources, or with pooled or arrayed screening.</li></ul>Our companyBioAge 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 https://bioagelabs.com. Our workplaceBioAge offers competitive salary, a comprehensive compensation package, and generous paid time off in addition to company-observed holidays. We provide comprehensive health and wellness benefits (medical, dental, and vision insurance) and a 401(k) retirement savings plan with matching employer contributions, and we support families through childcare and fertility benefits. We also pride ourselves in giving employees many opportunities for career development, including a generous annual budget for continued learning and a dedication to training and skill development. Many positions (including this one) are remote, allowing our team members to work from anywhere. The salary for this role is expected to be approximately $150,000 - $185,000, depending on experience. We are open to the concept that different experience levels add value to the team in different ways, and therefore will consider a variety of experience and offer commensurate pay. At BioAge, we embrace diversity and differences while we learn from each other, and we believe that every team member has an important role to play. We are an equal opportunity employer. BioAge prohibits discrimination and harassment of any type and affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. BioAge conforms to the spirit as well as to the letter of all applicable laws and regulations. </body></div>

Apply Now

Remote Opportunity

Translational AI Scientist/Engineer

Join BioAge Labs as a senior professional working remotely from United States. Explore the role, benefits, and apply in one place.

Translational AI Scientist/Engineer

BioAge Labs

Full Time

$150k - $185k

2 weeks ago

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.

United States

Senior

Generative AI

Agentic AI

Drug Discovery

+5 more

Job 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.

What we expect of you

Basic 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. We welcome scientist-first candidates with demonstrated ability to build production-quality AI workflows, as well as engineer-first candidates with deep translational-biology judgment.

Preferred technical qualifications

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).

Preferred scientific qualifications

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.

Also valued

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.

Our company

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 https://bioagelabs.com.

Our workplace

BioAge offers competitive salary, a comprehensive compensation package, and generous paid time off in addition to company-observed holidays. We provide comprehensive health and wellness benefits (medical, dental, and vision insurance) and a 401(k) retirement savings plan with matching employer contributions, and we support families through childcare and fertility benefits. We also pride ourselves in giving employees many opportunities for career development, including a generous annual budget for continued learning and a dedication to training and skill development. Many positions (including this one) are remote, allowing our team members to work from anywhere. The salary for this role is expected to be approximately $150,000 - $185,000, depending on experience. We are open to the concept that different experience levels add value to the team in different ways, and therefore will consider a variety of experience and offer commensurate pay.

At BioAge, we embrace diversity and differences while we learn from each other, and we believe that every team member has an important role to play. We are an equal opportunity employer. BioAge prohibits discrimination and harassment of any type and affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. BioAge conforms to the spirit as well as to the letter of all applicable laws and regulations.

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. we welcome scientist-first candidates with demonstrated ability to build production-quality ai workflows, as well as engineer-first candidates with 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.

Benefits

Health Insurance
Dental Insurance
Vision Insurance
Retirement Plan
Paid Time Off

Skills

Generative AI

Agentic AI

Drug Discovery

Python

LLM development

Retrieval-Augmented Generation

Orchestration

Translational science

Ready to Apply?

Join BioAge Labs today

Salary Range

$150k - $185k

Posted 2 weeks ago

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