Senior Software Engineer (AI/ML) (PhD / Postdoc)

About Truveta   Truveta provides unprecedented real-world data and real-time intelligence, powered by a dataset built with and owned by US health systems united in a mission of Saving Lives with Data. Together, we power breakthrough medical discoveries, accelerate regulatory-grade evidence, and improve patient care. Today, Truveta enables research on more than 130 million de-identified patients across the US.     Achieving Truveta’s ambitious mission requires an incredible team of talented and inspired people with a special combination of health, software and big data experience who share our  company values .    The Role   We are seeking a highly motivated  Postdoctoral Researcher  to explore and develop novel applications of cutting-edge AI/ML technologies on large-scale real-world clinical data.   This role is designed for recent PhD graduates who are passionate about pushing the boundaries of machine learning in healthcare. You will work at the intersection of  machine learning, clinical data, and biomedical science , identifying new opportunities where modern AI can unlock insights that were previously out of reach.   A key expectation is not just execution—but  imagination : the ability to envision and prototype entirely new ways to use rich patient data to solve meaningful healthcare problems.   What  You’ll  Do   Innovate: Identify and propose novel, high-impact applications of AI/ML using large-scale EHR data   Research & Prototype: Design, develop, and evaluate state-of-the-art models (e.g., foundation models, multimodal learning, causal ML, generative AI)   Work Across Modalities: Integrate structured and unstructured EHR data with emerging data types such as genomics, imaging, and clinical notes   Collaborate Cross-Functionally: Partner with clinicians, data scientists, and product teams to translate research ideas into real-world solutions   Publish & Share: Contribute to top-tier conferences/journals and represent Truveta in the research community   Explore the Unknown: Proactively identify problems that have not yet been addressed and define new research directions   What  We’re  Looking For   Minimum Qualifications   PhD in Computer Science, Machine Learning, Biomedical Informatics, or a related field   Strong background in machine learning, deep learning, or statistical modeling   Experience applying ML to healthcare, biomedical, or clinical datasets   Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow)   Preferred Qualifications   Experience with healthcare data (EHRs, claims, clinical notes) or biomedical data (genomics, imaging)   Familiarity with cutting-edge areas such as:    Foundation models / LLMs in healthcare   Multimodal learning   Causal inference   Representation learning on longitudinal data   Track record of publications in top-tier ML or healthcare venues (e.g., NeurIPS, ICML, ICLR, MLHC, AMIA)   Ability to work across disciplines and communicate with both technical and clinical stakeholders       Who You Are   Curious and imaginative:  You naturally think beyond existing solutions and ask “what hasn’t been done yet?”   Impact-driven:  You care about applying research to real-world healthcare problems   Comfortable with ambiguity:  You thrive in open-ended environments where defining the problem is part of the job   Collaborative:  You enjoy working across domains and learning from experts in different fields     Why This Role is Unique   Access to one of the largest and richest longitudinal EHR datasets in the US   Opportunity to work on  previously infeasible problems  at the intersection of AI and medicine   A balance of  academic-style research freedom  and  real-world impact   Collaboration with leading healthcare systems and industry partners     Example Problem Areas (Illustrative)   Learning patient trajectories using foundation models for clinical decision support   Discovering novel phenotypes or disease subtypes from multimodal data   Predicting treatment effectiveness using causal ML on real-world data   Generating synthetic cohorts for rare disease research   Integrating genomics + EHR for precision medicine applications