Is the "Senior Data Scientist, AI Retrieval Systems" position at Axle fully remote?

Yes, this is a remote position with no country requirement stated.

What skills are required for this Senior Data Scientist, AI Retrieval Systems position?

Key skills listed for this role include GA4.

How do I apply for the Senior Data Scientist, AI Retrieval Systems position at Axle?

You can view full details and apply for this position on WFA Digital at https://wfadigital.com/jobs/senior-data-scientist-ai-retrieval-systems-axle-38881.

Senior Data Scientist, AI Retrieval Systems

Axle·Remote

Data & Analytics

GA4

Job Description

(ID: 2026-3395)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

100% Medical, Dental & Vision Coverage for Employees
Paid Time Off and Paid Holidays
401K match up to 5%
Educational Benefits for Career Growth
Employee Referral Bonus
Flexible Spending Accounts:
- Healthcare (FSA)
- Parking Reimbursement Account (PRK)
- Dependent Care Assistant Program (DCAP)
- Transportation Reimbursement Account (TRN)

Axle is seeking a Senior Data Scientist, AI Retrieval Systems to join our vibrant team supporting rare disease research at the National Institutes of Health (NIH). This is a Remote position within the United States.

Position Summary:
Roughly 25 to 30 million people in the United States live with a rare disease. There are somewhere between 7,000 and 10,000 distinct rare conditions, and the large majority have no FDA-approved treatment. 
Research on these conditions keeps running into the same obstacles. Published evidence for any one disease is thin and scattered across sources. The same clinical finding gets written down a dozen different ways depending on who recorded it. And the people with the most at stake, patients and their families, are usually the least equipped to read the specialist literature written about their own condition. 
Large language models are well suited to this class of problem, and the research programs we support are investing in applying them carefully. In this role you will build the retrieval and knowledge layer that those AI systems stand on. That means the disease and phenotype vocabularies that give a model something precise to reason over, the semantic search that finds the right concept behind an imprecise human phrase, and the ranking that decides what a user sees first. Ontologies serve as internal scaffolding throughout. Users should never have to see one or learn what it is. 
This is a senior individual contributor position with unusual range. You will own the data layer, the retrieval services built on top of it, the interfaces where results become visible, and the path onto the computing infrastructure that runs it all. You will work directly with NIH program staff, clinical geneticists, and rare disease information specialists. 
Core Responsibilities:
 Model biomedical knowledge for rare disease research. Ingest disease and phenotype ontologies and controlled vocabularies into PostgreSQL with a maintainable release and refresh path, reconcile identifiers across sources, and work through term hierarchies to determine what is clinically relevant for a given condition. 
 
 Build retrieval-augmented services that ground everyday language in clinical concepts. Embed term labels, definitions, and synonyms, retrieve candidates, and have a model disambiguate against context before any value is committed.
 

 Treat retrieval as a database problem. Tune keyword and vector search over large biomedical corpora, and be ready to defend the recall and latency trade-offs you choose.
 
 Build the ranking and relevance layers that decide what surfaces first, including domain-aware weighting and graceful degradation when a condition falls outside curated coverage.
 
 Deliver the interfaces where this work becomes visible to users, in Next.js, React, and TypeScript. This covers question and confirmation flows, result presentation, and live status for long-running pipelines.
 
 Deploy continuously onto NIH on-premises and high-performance computing Kubernetes environments. Helm charts, StatefulSets, secrets, ingress, GPU scheduling for self-hosted inference, and scheduled jobs are all in scope, and you will partner with the operations teams that run those environments instead of standing up parallel cloud infrastructure.
 
 Build the evaluation that tells us whether retrieval and concept mapping are good enough to rely on, and keep it running as a regression suite instead of a one-time measurement.
 
 Log what the system does and why.

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Senior Data Scientist, AI Retrieval Systems

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