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About This Role
BioAge Labs (BIOA) is finding new ways to treat disease by targeting the mechanisms of aging, with the ultimate goal of increasing healthy human lifespan. https://bioagelabs.com
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.
Salary Context
This $150K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At BioAge Labs, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $150K to $185K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
BioAge Labs AI Hiring
BioAge Labs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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