{"id":2634258,"url":"https://alion.io/job/axleinfo-senior-data-scientist-agentic-ai-systems","title":"Senior Data Scientist, Agentic AI Systems","company":{"id":4416199,"name":"Copyright Axle Informatics","domain":"axleinfo.com","url":"https://alion.io/company/axleinfo","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":80,"open_postings":6,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":10,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":80000,"max_usd":179000,"period":"year","method":"global_role_seniority_cell","sample_n":2491},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Pydantic","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Anthropic","optional":true},{"name":"Braintrust","optional":true},{"name":"LangChain","optional":true},{"name":"Langfuse","optional":true},{"name":"LangGraph","optional":true},{"name":"LangSmith","optional":true},{"name":"LiteLLM","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenAI","optional":true},{"name":"Pydantic AI","optional":true}],"status":"live","first_seen_at":"2026-08-26T16:33:16Z","employer_posted_date":"2026-08-26","last_verified_at":"2026-10-20T10:05:27Z","board_verified":true,"closed_at":null,"days_open":54,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":54},"description":"(ID: 2026-3432)\nAxle 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).\nBenefits We Offer:\n100% Medical, Dental & Vision Coverage for Employees\nPaid Time Off and Paid Holidays\n401K match up to 5%\nEducational Benefits for Career Growth\nEmployee Referral Bonus\nFlexible Spending Accounts:Healthcare (FSA)\nParking Reimbursement Account (PRK)\nDependent Care Assistant Program (DCAP)\nTransportation Reimbursement Account (TRN)\n\nAxle is seeking a Senior Data Scientist, Agentic AI 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. \nPosition Summary \nRoughly 25to 30 million peoplein 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. \nResearch 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 waysdepending on who recorded it. And the people with the most at stake, patients and their families, are usually the least equipped to read the specialistliterature written about their own condition. \nLarge 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 conversational AI systems that sit between a person and the research infrastructure. These are multi-turn workflows that ask sensible follow-up questions in plain language, capture the answers as validatedstructured data, and hand that structure off to the searches and analyses doing the scientific work. The emphasis is on systems people can rely on, which in practice means confirming every interpretation before it is saved and logging every automated decision so that it can be reviewed later. \nThis is a senior individual contributor position. You will own major components from design through deployment, work directly with NIH program staff, clinical geneticists, and rare disease information specialists, and help set the engineering standards for how AI gets applied onthis team. \nCore Responsibilities \nBuild agentic AI systems for rare disease research workflows. This includes the conversation logic, the rules that decide when enough information has been gathered, and the confirmation steps that catch a misreading before it reaches anything downstream. \nModel outputs in Pydanticand use structured output and tool calling, so that every field a model produces istyped, validated, and traceable back to its source. \nWrite, version, and regression test the prompts behind clinical and scientific reasoning tasks. Prompts and output schemas are treated as code here, with tests to match. \nBuild evaluation for tasks that have no single right answer. Golden sets, offline regression suites, and model-based graders all have a place, and the results should be good enough to decide what ships. \nKeep multi-step LLM workflows responsive under load. This covers async design, concurrency limits, streaming partial results to the client, and timeout and failure handling that holds up in production. \nLogwhat the system does and why. Request identifiers, latency, errors, and the reasoning behind each automated choice all need to be captured, so that staff can review an AI-assisted result instead of taking it on faith. \nWork out what researchers, clinicians, and patient communities need, and turn it into data models and system behavior. \nWrite the work up. You will contribute to manuscripts, conference abstracts, and posters with NIH investigators, and you will be credited as an author on work you helped produce. \nRequired Qualifications \nBachelor’s degree in Data Science, Computer Science, Bioinformatics, Biomedical Informatics, or a related field. An advanced degree is preferred. We will consider equivalent professional experience in place of a degree. \nAt least 5 yearsbuilding and operating production software or data systems. At least 2 of those years should involve shipping LLM-powered applications (agents, retrieval, or evaluation) that people depend on. We weigh depth in agentic workflow engineering more heavily thantotal years. \nExperience with structured output and tool or function calling, meaning you have constrained a model to a typed schema and validatedwhat came back. \nExperience evaluating systems that have no single right answer, using golden sets, offline regression suites, or model-based graders to decide whether a change was an improvement. \nAbility to own a service end to end, from schema design through deployment and operation. \nAbility to obtain and maintaina Public Trust Security clearance. \nTechnical Skills \nPython, with FastAPI, Pydantic, and pytest. \nLLM application engineering: provider APIs and gateways, prompt and context design, structured generation, tool use, and tracing. \nPostgreSQL, including work with embeddings or vector search alongside relational data. \nAsynchronous and concurrent Python, plus streaming results to a client. \nContainers and Kubernetes, enough to ship, debug, and operatea service on infrastructure you do not administer. \nGit-based collaboration and CI/CD in a shared codebase. \nPreferred Skills \nA typed agent framework such as PydanticAI, LangGraph, or the OpenAI or Anthropic agent SDKs, and MCP for tool integration. \nLLM tracing and evaluation tooling such as Langfuse, LangSmith, ArizePhoenix, or Braintrust. \nServing open-weight models in production with Ollamaor vLLMbehind a gateway such as LiteLLM. \nBiomedical ontologies and controlled vocabularies, including MONDO, HPO, UMLS, MeSH, and other OBO Foundry resources, along with comfort working through term hierarchies, synonyms, and cross references. \nBackground in rare disease, clinical genetics, or translational research. \nExperience working alongside clinicians, curators, or patient advocacy organizations, and translating their vocabulary into a data model that holds up. \nPublished or presented work that explains your engineering to people who did not build it. Peer-reviewed papers, conference talks, preprints, technical blog posts, and public open sourcecontributions all count. \nPrior or current NIH experience. \nWe are looking for an engineer first. If you have shipped LLM systems that people depend on and have never opened an ontology file, we want to hear from you. Rare disease and ontology background isuseful but not required, and we expect to teach the domain to whoever we hire. Candidates who meet the required qualifications and none of the preferred ones are encouraged to apply. \nDisclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.\nThe diversity of Axle’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.\nAccessibility: If you need an accommodation as part of the employment process please contact: \nThis role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.\nSalary Range\n$130,000—$150,000 USD","description_format":"text","description_chars":8558,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":true,"languages":[]},"benefits":["401k plan"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-15T16:58:48Z"}],"visa":[],"liveness":{"score":9,"band":"cold","label":"Long shot","p_open":1,"p_active":0.245,"p_room":0.35,"age_days":54,"expected_fill_days":10,"reasons":["conf:0","win:tail"],"computed_at":"2026-10-20T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/axleinfo-senior-data-scientist-agentic-ai-systems","json_url":"https://alion.io/job/axleinfo-senior-data-scientist-agentic-ai-systems.json","meta":{"generated_at":"2026-10-20T11:53:45Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":13182,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-21T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4416199},"rest":"https://alion.io/mcp/rest/get_company?id=4416199"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Faxleinfo-senior-data-scientist-agentic-ai-systems"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Faxleinfo-senior-data-scientist-agentic-ai-systems"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Faxleinfo-senior-data-scientist-agentic-ai-systems"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/axleinfo-senior-data-scientist-agentic-ai-systems\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Faxleinfo-senior-data-scientist-agentic-ai-systems"}]}