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Johnson & Johnson Innovative Medicine is seeking a Translational Post Doctoral Researcher – Agentic AI for Neurodegeneration for a 2‑year fixed‑term position. This position can be located in either Raritan, New Jersey; Titusville, New Jersey; Spring House, Pennsylvania; San Diego, California or Cambridge, Massachusetts (no fully remote option).

Location: Cambridge, Massachusetts; Raritan, New Jersey; Titusville, New Jersey; Spring House, Pennsylvania; San Diego, California.

The role will be embedded in the Machine Intelligence (MI) team at J&J Innovative Medicine, working in partnership with the c-brAIn academic network. The researcher will engage with multi‑modal neuroscience data—understanding each modality, building evaluation frameworks, and partnering with translational and experimental teams at Washington University in St. Louis and other partner institutions. Mentorship is designed to develop leaders at the intersection of Multi‑Modal Data, AI Evaluation, and Neurodegeneration.

• Multi‑Modal Data Integration: Characterize and integrate biomedical data modalities—including digital pathology, neuroimaging, omics, and longitudinal clinical data—to develop specialized, domain‑specific models for neurodegeneration.
• Multi‑Modal Data Integration: Build and refine data engineering pipelines that harmonize heterogeneous modalities—reconciling differences in spatial resolution, temporal scale, and dimensionality—into unified analytical frameworks.
• Multi‑Modal Data Integration: Identify where cross‑modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation.
• Agentic AI Evaluation: Critically assess AI‑driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities.
• Agentic AI Evaluation: Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings or failed clinical trials and evaluate whether these integrations generate genuinely novel hypotheses.
• Agentic AI Evaluation: Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities—assessing whether models can reason credibly across imaging, omics, and clinical evidence.
• Agentic AI Evaluation: Develop benchmarks using synthetic and real‑world multi‑modal datasets that probe AI co‑scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert‑level biomedical reasoning.
• Research & Communication: Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions.
• Research & Communication: Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives.
• Research & Communication: Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS).
• Research & Communication: Articulate emerging AI/ML approaches—causal reasoning, intent classification, agentic planning—to diverse audiences with clear framing of practical applications in drug discovery.
• Research & Communication: Co‑author manuscripts, concept papers, and translational strategy documents.

• Ph.D. (or M.D./Ph.D.) in neuroscience, neurobiology, computational neuroscience, biomedical informatics, or a closely related field (degree completed within the last three years or to be completed within the next six months).
• Deep knowledge of neurodegenerative disease biology (Alzheimer’s, Parkinson’s, etc.) including disease mechanisms, experimental models, and translational challenges.
• Hands‑on experience working with at least two of the following data modalities in a research context: neuroimaging (PET, MRI), digital pathology, omics, longitudinal clinical data.
• Familiarity with large language model architectures and agentic AI frameworks (e.g., LangGraph, DSPy, or equivalent orchestration tools).
• Proficiency in Python and common machine‑learning/data‑engineering frameworks.
• Excellent scientific communication skills and comfort working across computational, translational, and experimental teams.
• Self‑directed, with the ability to work both independently and within a diverse, multi‑disciplinary team.

• Experience building data pipelines that integrate heterogeneous biomedical data types.
• Familiarity with evaluation or benchmarking methodologies for AI/ML systems.
• Experience with natural language processing techniques: named entity recognition, natural language inference, knowledge graph construction.
• Knowledge of graph data structures, graph analytics, and graph platforms (Neo4j, Neptune).
• Familiarity with cloud infrastructure (AWS and/or Azure) for scalable pipelines.

• Base pay range: $79,000 – $127,650 for positions in the United States.
• Eligible for an annual performance bonus in accordance with the applicable plan.
• Health, dental, vision, life insurance, short‑ and long‑term disability, and business accident insurance.
• Group legal insurance and consolidated retirement plan (401(k)).
• Time‑off benefits: up to 120 hours of vacation per calendar year, up to 40 hours of sick time, up to 13 days of holiday pay (including floating holidays), up to 40 hours of work, personal and family time per calendar year.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants may contact us via the company’s contact page and internal employees contact the accommodation resource.