Principal Scientist, Translational AI Foundations Full-time

Principal Scientist, Translational AI Foundations

Translational IPS is forming Translational AI Foundations (TAIF), a small team of computational biologists and AI engineers that works side by side with our translational research groups. TAIF turns the biomarker analyses scientists already perform by hand into validated, reusable AI agents and workflows, then transfers each one to the research group that will use and maintain it. The science stays with those groups; TAIF builds the shared tools that make it faster and reproducible.

We are seeking a Principal Scientist to own the shared software platform the whole team builds on: the pieces no translational research group should have to build for itself. You will turn the team's engineering standards into tooling, own the ontology and knowledge graph as a shared service, and build versioned connections to our governed data. You will deploy validated tools as reliable services (REST APIs, tool servers, containers) with CI/CD, monitoring and a rollback plan. The initial scientific focus is circulating tumor DNA (ctDNA): its data connection and reference dataset, then, after the first spatial agents, a ctDNA agent built with the team's assay scientist. Deployment here means engineering deployment; broader adoption in the research groups is led by the team lead, with your hands-on support.

This role is for an architect who still builds: someone who has shipped governed data foundations and production services that scientists depend on, who understands the cost and value of regulated traceability (audit trails, versioning, validation evidence), and who has started building agentic automation on that foundation. You will build on the existing enterprise data platforms in partnership with the data, IT and knowledge-management teams that own them.

Your contributions will speed translational decisions across the BMS early clinical portfolio and help bring new medicines to patients sooner.

You will apply these resources across two areas:

• The shared platform and knowledge systems. The majority of the role: the shared software platform, engineering standards as tooling, the ontology and knowledge graph, and the connections to governed data.
• Deployment, hand-off and measurement. The remainder: deploying every validated tool, packaging it for the team that will own it, usage measurement, and direct work with translational research groups.

Key Responsibilities

• The shared platform and knowledge systems
• Shared software platform. Build and maintain provenance tracking (data, code, model and prompt versions behind every number), evaluation tooling, access control, logging and usage telemetry, to enterprise security and release standards.
• Engineering standards as tooling. Implement the tool template, the automated test and validation pipeline, packaging of tools for use by AI agents (for example as Model Context Protocol tool servers), and a registry of tools with their owners, versions, lifecycle stages and users.
• Knowledge systems. Own the ontology and the knowledge graph service that hold the team's canonical biomarker definitions (their scientific content comes from the assay scientist). Start with a biomarker-trial ontology covering subjects, cohorts, visits, specimens, assays, endpoints and cutoffs, starting from the definitions scientists already use and connected to the enterprise data catalog.
• Data connections and governance. Build and maintain approved, versioned connections to the governed data products and biomarker data stores, recording the data version on every read; map clinical studies into a common data model; coordinate data logistics and governance with the data owners, starting with ctDNA.
• Deployment, hand-off and measurement
• Deployment and operations. Containerize and deploy each validated tool and service (Docker; AWS ECS or Kubernetes on EKS, or the platform the owning team runs) with CI/CD, monitoring and a rollback plan, and own the release checklist (validation, access, monitoring, support owner, rollback) with the platform teams.
• Hand-off packaging and measurement. Package every component for the team that will own it (documentation, tests, versioning, a registry entry linked to its test cases) so it can be taken over unchanged. Instrument every tool for usage reporting (users, teams, elapsed and review time, test results, use after hand-off) and report usage and impact metrics.
• Research-group partnership. Work with the AI contacts in translational research groups: run hands-on tool-building sessions and review the tools they build against the team's engineering standards and for deployability.
• Engineering influence. Set the engineering practice the team and the AI contacts follow (code review, tests, versioning, release discipline), mentor junior team members on it, and document the shared platform so that its owning team can run it without you.

Basic Qualifications

• Ph.D. in computational biology, bioinformatics, computer science, data engineering or a related quantitative field with 4+ years of relevant academic and/or industry experience
• Or Master's degree with 6+ years
• Or Bachelor's degree with 8+ years.

Preferred Qualifications

• Ph.D. in computational biology, bioinformatics, computer science, data engineering or a related quantitative field with 4+ years of relevant academic and/or industry experience
• A record of shipping production data systems or services that other teams depend on: Python services, SDKs or pipelines with tests, version control and code review.
• Hands-on cloud engineering on AWS or an equivalent provider: storage and compute (for example S3, Batch, Lambda), containers (Docker), infrastructure as code, CI/CD, and monitoring and troubleshooting of running services.
• Experience designing governed FAIR (findable, accessible, interoperable, reusable) data foundations with their owners: schema and entity modeling, metadata harmonization, data-version and lineage discipline, access control and retention.
• Working familiarity with translational biomarker data (sequencing-based assays, liquid biopsy, clinical and sample metadata) sufficient to model it correctly, and with GxP-relevant traceability expectations (audit trails, versioning, validation evidence, human-review records).
• Experience building LLM-based or agentic automation, or building the data connectors, benchmark datasets and evaluation that such systems depend on, with the engineering judgment to run it as a maintained service.
• Agentic systems in production: tool servers (for example Model Context Protocol), retrieval and grounding, state and memory, evaluation harnesses, regression testing and telemetry, with several model providers behind one gateway (for example Amazon Bedrock).
• Knowledge graph and ontology engineering: graph databases, controlled terminologies, consortium data dictionaries and standards (for example BloodPAC for liquid biopsy), schema validation (for example SHACL or JSON Schema), and provenance models.
• Container orchestration and platform engineering at scale: Kubernetes, EKS or ECS, Terraform, event-driven architectures, workflow managers (for example Nextflow with Seqera Platform) and secondary-analysis platforms such as DRAGEN.
• Experience with governed data products (for example on Databricks) and data-version discipline; observability and cost management of AI services in the cloud.
• Regulated delivery: companion-diagnostic or IVD validation, quality-management-system requirements, CAPA resolution, or traceable evidence for IND or marketing submissions.
• Experience in a platform or enablement team that served scientists directly and measured success by adoption rather than delivery: workshops, office hours, governed SDKs that bench scientists used without managing the infrastructure. Front-end experience (for example Streamlit, JavaScript or TypeScript) is useful, not required.
• Experience leading engineers or cross-functional delivery across assay development, biostatistics, clinical, IT, legal and regulatory stakeholders.
• Prior experience in translational oncology or early clinical development at a biopharmaceutical or diagnostics company.