# Senior Data Scientist, AI Retrieval Systems

**Company:** [Axle](https://hotfix.jobs/companies/axle)
**Location:** Remote
**Role:** Data Science
**Salary:** $130k – $150k/yr
**Experience:** 5+ years
**Skills:** Python, FastAPI, Pydantic, Pytest, Postgres, Pgvector, Vector Search, Full-Text Search, Embeddings, Kubernetes, Helm, React, TypeScript, Next.js, Llm Engineering
**Posted:** 2026-08-26

> Build and operate biomedical retrieval, ranking, and concept-mapping systems that ground LLM applications for rare disease research. The role requires at least five years of production software or data-systems experience, including two years delivering LLM-powered applications, plus strong retrieval and Kubernetes skills.

## Job Description

## Responsibilities
- Model biomedical knowledge for rare disease research by ingesting disease and phenotype ontologies and controlled vocabularies into PostgreSQL, reconciling identifiers, and maintaining release and refresh workflows.
- Build retrieval-augmented services that map everyday language to clinical concepts using embeddings, candidate retrieval, contextual model disambiguation, structured outputs, and validation.
- Tune keyword and vector search over large biomedical corpora, balancing recall and latency.
- Build ranking and relevance layers with domain-aware weighting and graceful degradation outside curated coverage.
- Deliver user interfaces with Next.js, React, and TypeScript, including question and confirmation flows, result presentation, and live pipeline status.
- Deploy and operate services on NIH on-premises and high-performance computing Kubernetes environments, including Helm charts, StatefulSets, secrets, ingress, GPU scheduling, and scheduled jobs.
- Build evaluation and regression systems using golden sets and retrieval metrics such as Recall@K and MRR.
- Instrument systems with request identifiers, latency, errors, and selected concepts for reviewable AI-assisted results.
- Translate researcher, clinician, and patient needs into data models, retrieval behavior, and interface design.
- Contribute to manuscripts, conference abstracts, and posters with NIH investigators.

## Requirements
- Bachelor’s degree in Data Science, Computer Science, Bioinformatics, Biomedical Informatics, or a related field; equivalent professional experience may be considered.
- At least 5 years building and operating production software or data systems, including at least 2 years shipping LLM-powered applications.
- End-to-end retrieval systems experience, including indexing, query construction, and retrieval-quality measurement.
- Experience evaluating systems without a single correct answer using golden sets, offline regression suites, Recall@K, MRR, or similar metrics.
- Experience with structured output and tool or function calling using typed schemas and validation.
- Ability to own services from schema design through deployment and operation.
- Ability to obtain and maintain a Public Trust Security clearance.
- Proficiency with Python, FastAPI, Pydantic, pytest, PostgreSQL, vector and full-text search, embedding pipelines, containers, Kubernetes, Next.js, React, TypeScript, Git, and CI/CD.

## Nice-to-haves
- Biomedical ontologies and controlled vocabularies, including MONDO, HPO, UMLS, MeSH, and OBO Foundry resources.
- Entity linking, concept normalization, ontology alignment, or embedding-based domain grounding.
- Helm and deployment to on-premises or HPC Kubernetes environments.
- Production serving of open-weight models with Ollama or vLLM, LiteLLM, and domain embedding models such as MedCPT.
- Rare disease, clinical genetics, or translational research experience.
- Contributions to biomedical resources, standards, or consortia such as OBO Foundry or GA4GH.
- Published or presented engineering work, open-source contributions, or prior NIH experience.

## Compensation and Benefits
- Annual salary: $130,000–$150,000.
- 100% employee medical, dental, and vision coverage.
- Paid time off and holidays.
- 401(k) match up to 5%.
- Educational benefits, employee referral bonus, and flexible spending accounts.

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