Note: The job is a remote job and is open to candidates in USA. Axle is a bioscience and information technology company focused on translational research, biomedical informatics, and data science applications for research centers and healthcare organizations. The Senior Data Scientist, Agentic AI Systems will build and operate conversational AI systems for rare disease research, owning major components from design through deployment and collaborating with NIH staff, clinical geneticists, and rare disease information specialists.
Responsibilities
- Build 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
- Model outputs in Pydantic and use structured output and tool calling, so that every field a model produces is typed, validated, and traceable back to its source
- Write, 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
- Build 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
- Keep 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
- Log what 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
- Work out what researchers, clinicians, and patient communities need, and turn it into data models and system behavior
- Write 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
Skills
- Bachelor's degree in Data Science, Computer Science, Bioinformatics, Biomedical Informatics, or a related field
- We will consider equivalent professional experience in place of a degree
- At least 5 years building 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
- Experience with structured output and tool or function calling, meaning you have constrained a model to a typed schema and validated what came back
- Experience 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
- Ability to own a service end to end, from schema design through deployment and operation
- Ability to obtain and maintain a Public Trust Security clearance
- Python, with FastAPI, Pydantic, and pytest
- LLM application engineering: provider APIs and gateways, prompt and context design, structured generation, tool use, and tracing
- PostgreSQL, including work with embeddings or vector search alongside relational data
- Asynchronous and concurrent Python, plus streaming results to a client
- Containers and Kubernetes, enough to ship, debug, and operate a service on infrastructure you do not administer
- Git-based collaboration and CI/CD in a shared codebase
- An advanced degree is preferred
- We weigh depth in agentic workflow engineering more heavily than total years
- A typed agent framework such as Pydantic AI, LangGraph, or the OpenAI or Anthropic agent SDKs, and MCP for tool integration
- LLM tracing and evaluation tooling such as Langfuse, LangSmith, Arize Phoenix, or Braintrust
- Serving open-weight models in production with Ollama or vLLM behind a gateway such as LiteLLM
- Biomedical 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
- Background in rare disease, clinical genetics, or translational research
- Experience working alongside clinicians, curators, or patient advocacy organizations, and translating their vocabulary into a data model that holds up
- Published 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 source contributions all count
- Prior or current NIH experience
Benefits
- 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
- Healthcare (FSA)
- Parking Reimbursement Account (PRK)
- Dependent Care Assistant Program (DCAP)
- Transportation Reimbursement Account (TRN)
Company Overview
Company H1B Sponsorship