Principal Research AI Innovation Lead

Responsibilities

• Partner with scientists and research leaders to identify high-impact opportunities where AI can improve research speed, quality, consistency, traceability, and decision-making.
• Help shape multi-year Gen AI strategies, lead workstreams, and establish reusable building blocks – agentic frameworks, evaluation harnesses, retrieval and grounding components, tool servers, prompt and policy libraries, and provenance infrastructure – on which research programs build.
• Architect and personally implement the agentic system-of-systems that executes complex, long-horizon scientific workflows across research, including target evidence assembly, indication rationale construction, biomarker interpretation, translational synthesis, literature and evidence triangulation, and decision support, with explicit attention to inter-agent coordination, state and memory management, verification, recovery from intermediate failure, and lifecycle governance of agents in production.
• Establish the scientifically rigorous evaluation, benchmarking, and reliability standards that critical research AI systems are expected to meet, including curated benchmark datasets, expert-reviewed reference standards, rubric-based assessments, hallucination and grounding metrics, calibration of uncertainty, longitudinal monitoring, regression gating in production, and the governance under which those standards are applied.
• Design mechanisms for incorporating expert feedback, scientific rationale, provenance, and research context into AI workflows so that systems and institutional knowledge improve over time.
• Collaborate with engineering, data, IT, security, legal, vendor, and platform teams to ensure prototypes are designed with appropriate governance, integration paths, and scalability in mind.
• Communicate AI opportunities, risks, limitations, evidence quality, and results clearly to scientific, technical, and executive audiences.
• Stay current with emerging AI methods, tools, vendors, and industry practices, and assess where they can create practical value for Research.

Requirements

• Bachelor's Degree
• 8+ years of academic / industry experience
• Or Master's Degree 6+ years of academic / industry experience
• Or Ph D 4+ years of academic / industry experience
• Advanced degree, such as MS, Ph D, Pharm D, or equivalent experience in a scientific, computational, or AI-related field.
• Direct experience in one or more research areas such as target identification and evaluation, indication expansion, drug repurposing, biomarker discovery, translational research, clinical evidence review, or portfolio decision support.
• Ability to interpret scientific evidence, assess analytical quality, and evaluate whether AI-generated scientific outputs are grounded, appropriately caveated, and defensible.
• Substantive hands‑on architectural depth in modern AI and large language model methods, including agentic workflows, multi-agent orchestration, long‑horizon task execution, Graph RAG, Model Context Protocol (MCP) auth patterns, and deep research workflows with reasoning models, with the depth to define reference architectures and architectural standards rather than to integrate vendor APIs.
• Experience building AI systems that reason across heterogeneous scientific evidence, including genetic associations, clinical outcomes, literature, omics data, assay data, real‑world data, or other biomedical data sources.
• Experience designing AI evaluation frameworks, benchmark datasets, human-reviewed reference standards, rubric-based assessments, or scientific quality metrics.
• Experience developing or adapting deep learning models for biological, biomedical, or translational research applications, including fine‑tuning biology-focused large language models, multimodal generative models, protein or sequence foundation models, representation-learning models, or other domain-specific AI systems.
• Experience working in innovation-lab, accelerator, startup, skunkworks, or rapid-prototyping environments.
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