Senior Data Scientist, AI Retrieval Systems

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist, AI Retrieval Systems based in United States.

This is a senior individual contributor opportunity focused on building the retrieval and knowledge infrastructure behind AI systems supporting rare disease research.
You will develop biomedical knowledge models, semantic retrieval, ranking systems, and AI-powered services that help researchers and clinicians find and interpret complex information.
The role spans the full technical lifecycle, from data modeling and evaluation through application development, deployment, observability, and ongoing operations.
You will work directly with research stakeholders, clinical geneticists, and rare disease information specialists to turn real-world needs into reliable technical solutions.
Your work will run in demanding on-premises and high-performance computing environments, giving you ownership well beyond a typical data science role.
You will also contribute to scientific publications, conference materials, and research outputs alongside domain experts.
This role is ideal for an engineer-minded data scientist who enjoys solving ambiguous retrieval problems and building AI systems that people can depend on.

Accountabilities:

• Build and maintain biomedical knowledge models for rare disease research, ingesting disease and phenotype ontologies and controlled vocabularies into PostgreSQL while reconciling identifiers, hierarchies, synonyms, and cross-references.
• Develop retrieval-augmented services that translate everyday language into precise clinical concepts using embeddings, semantic search, model-based disambiguation, and structured outputs.
• Design, tune, and operate keyword and vector retrieval systems, balancing recall, relevance, latency, and scalability across large biomedical datasets.
• Build ranking and relevance layers that determine which concepts and results surface first, incorporating domain-aware weighting and graceful handling of gaps in curated coverage.
• Develop user-facing experiences in Next.js, React, and TypeScript, including question and confirmation flows, result presentation, and status interfaces for long-running processing pipelines.
• Deploy and maintain services across on-premises and high-performance computing Kubernetes environments, working with Helm, StatefulSets, secrets, ingress, GPU scheduling, and scheduled workloads.
• Partner with infrastructure and operations teams to ensure reliable deployments without creating parallel cloud infrastructure.
• Establish rigorous evaluation frameworks for retrieval and concept-mapping quality, using regression suites and measurable benchmarks to detect degradation and validate improvements.
• Implement observability that captures request identifiers, latency, errors, selected concepts, and other decision signals so AI-assisted results can be reviewed and understood.
• Collaborate with researchers, clinicians, and patient-focused stakeholders to understand their needs and translate them into data models, retrieval behavior, and intuitive interfaces.
• Contribute to manuscripts, conference abstracts, posters, and other research outputs, receiving authorship for meaningful contributions.

Requirements:

• Bachelor’s degree in Data Science, Computer Science, Bioinformatics, Biomedical Informatics, or a related discipline, or equivalent professional experience; an advanced degree is preferred.
• At least 5 years of experience building and operating production software or data systems, including at least 2 years shipping LLM-powered applications such as agents, retrieval systems, or evaluation frameworks.
• Strong end-to-end retrieval engineering experience covering indexing, query construction, retrieval optimization, and measurement against real-world data.
• Experience evaluating AI or retrieval systems where there is no single correct answer, using golden datasets, offline regression suites, View email address on us.fitly.work, MRR, or comparable metrics.
• Hands-on experience with structured model outputs, tool/function calling, typed schemas, and validation of model-generated data.
• Ability to own services across their lifecycle, from schema and data design through deployment, monitoring, troubleshooting, and operation.
• Strong Python skills, including experience with FastAPI, Pydantic, and pytest.
• Deep PostgreSQL experience, including vector search with pgvector or equivalent, full-text search, embedding pipelines, indexing, and query optimization.
• Practical expertise in LLM application engineering, including provider APIs and gateways, prompt and context design, structured generation, and tool use.
• Experience building repeatable data ingestion and transformation pipelines with reliable refresh processes.
• Working knowledge of containers and Kubernetes sufficient to deploy, debug, and operate services in infrastructure you do not directly administer.
• Working proficiency with Next.js, React, and TypeScript.
• Experience collaborating through Git-based workflows and CI/CD in shared codebases.
• Ability to obtain and maintain a Public Trust Security clearance .
• Preferred experience includes biomedical ontologies and controlled vocabularies such as MONDO, HPO, UMLS, MeSH, or OBO Foundry resources.
• Familiarity with entity linking, concept normalization, ontology alignment, or embedding-based domain grounding is advantageous.
• Experience with Helm and on-premises or HPC Kubernetes deployments is a plus.
• Exposure to serving open-weight models using technologies such as Ollama or vLLM, model gateways such as LiteLLM, or biomedical embedding models such as MedCPT is beneficial.
• Background in rare disease, clinical genetics, translational research, NIH programs, or contributions to biomedical standards and open resources is preferred.
• Published or presented technical or research work, including papers, conference talks, preprints, technical articles, or open-source contributions, is a plus.

Benefits:

• Competitive salary: Anticipated base compensation of $130,000–$150,000 USD , with actual compensation based on experience, qualifications, skills, and location.
• Medical coverage: 100% medical, dental, and vision coverage for employees.
• Retirement savings: 401(k) plan with employer matching of up to 5% .
• Paid time off: PTO and paid holidays.
• Professional development: Educational benefits designed to support career growth.
• Flexible spending accounts: Access to healthcare FSA, parking reimbursement, dependent care assistance, and transportation reimbursement programs.
• Employee referral bonus: Additional rewards for successful employee referrals.
• Remote work: Fully remote position available within the United States.
• Research impact: Opportunity to contribute to meaningful rare disease research alongside scientific and clinical experts.
• Publication opportunities: Contributions to manuscripts, conference abstracts, posters, and other research outputs.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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