Research AI Engineer

Overview

Prof. Marinka Zitnik invites applications for a Research AI Engineer position at Harvard University.

We are seeking a talented Research AI Engineer to implement, test, and deploy customized biomedical AI tools. This role involves designing cutting-edge software solutions that accelerate biomedical discovery and support lab operations. The engineer will work closely with graduate students, postdoctoral fellows, biologists, and clinicians to develop transformative AI systems.

Responsibilities:

  • Develop model pipelines for LLMs and foundation models using multi-GPU training and inference.
  • Design and implement large-scale data pipelines optimized for precision, recall, and processing speed.
  • Train and orchestrate multi-agent LLM systems, including model fine-tuning and custom embedding creation.
  • Develop an experimentation platform for medical AI tools.
  • Test diverse AI system designs and optimize performance across benchmarks.
  • Generate new ideas and develop solutions to maximize performance on a broad range of benchmarks.

This position offers an opportunity to contribute to groundbreaking research, using state-of-the-art AI to address critical problems in science.

Interested candidates are encouraged to explore our recent publications and research directions before applying.

Qualifications

Required skills and experience:

  • Strong experience across ML and LLM software stack, including feature engineering, model development, deployment, and validation.
  • Proficiency in deep learning frameworks such as PyTorch, with openness to learning new tools and technologies.
  • Experience in training embeddings with custom evaluation datasets.
  • Familiarity with custom agents and techniques such as retrieval-augmented generation.
  • Experience using distributed systems for large-scale training or inference.
  • Experience with LLMs for training, fine-tuning, or inference.

Strong candidates will have a keen desire to leverage advanced AI technologies to address scientific challenges. They will have excellent communication and collaboration skills to work effectively in a multidisciplinary, fast-paced environment.

Publications in machine learning or AI conferences, or scientific journals, are a strong plus.

Candidates must hold a Ph.D. in computer science, engineering, or a closely related field. Exceptional candidates with a Master’s degree and significant relevant experience will also be considered.

Location

On campus of Harvard Medical School, Boston, MA.

Application process

Interested applicants should submit the following documents via email to Prof. Zitnik and use the subject line “Research AI Engineer”:

  • Cover letter
  • Curriculum Vitae (include links to your academic webpage and GitHub repositories)
  • Three representative examples of past work
  • Three letters of recommendation (will be solicited after the initial review)

We are reviewing applications on a rolling basis. Interested candidates are encouraged to submit their applications early.

Advisor

Marinka Zitnik is an Associate Professor of Biomedical Informatics at Harvard Medical School, Associate Faculty at Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, and Associate Member at Broad Institute of MIT and Harvard. Zitnik investigates foundations of AI that contribute to the scientific understanding of medicine and therapeutic design, eventually enabling AI to learn and innovate on its own. Her research won best paper and research awards, including the Overton Prize, Kavli Fellowship of the National Academy of Sciences, Kaneb Fellowship award at Harvard Medical School, NSF CAREER Award, awards from the International Society for Computational Biology, International Conference in Machine Learning, Bayer Early Excellence in Science, Amazon Faculty Research, Google Faculty Research, Roche Alliance with Distinguished Scientists, and two Sanofi iDEA-iTECH Awards.

Latest News

Jul 2026:   Immune Checkpoint Inhibitors in Nature Medicine

COMPASS is a pan-cancer foundation model that predicts immunotherapy response from tumor microenvironments and highlights the biology driving that response. [Nature Medicine paper] [Harvard Medicine News]

Jul 2026:   ATHENA Agent for Treatment Reasoning

Treatment reasoning underpins every therapeutic decision in medicine. ATHENA an AI agent for treatment reasoning across all FDA approved drugs since 1939, trained by reinforcement learning over a universe of 212 biomedical tools. [Project website]

Jun 2026:   MedLog

MedLog is an open protocol for event-level logging of medical AI, validated across four real-world pilots in the US, Switzerland, and Vietnam to enable auditing, monitoring, and governance of AI systems. [Paper] [Project website]

Jun 2026:   Biological Reasoning Models

Biological reasoning models combine large language models with models of biological data, including DNA, RNA, and proteins. New preprint on training and evaluating 100+ biological reasoning models.

Apr 2026:   OptimusKG: A Modern Knowledge Graph

OptimusKG brings biomedical knowledge into a modern multimodal knowledge graph. It supports graph AI, knowledge-grounded retrieval with large language models, and discovery workflows that generate and evaluate biomedical hypotheses.

Apr 2026:   ARK Accepted at ACL 2026

Mar 2026:   Open 'AI Scientists' Initiative

Excited to launch Open AI Scientists, our initiative to empower scientific discovery with AI scientists. [https://www.openscientist.ai]

Mar 2026:   Generalist Biological AI in Nature Biotechnology

Mar 2026:   Claw Institute

Claw Institute is a research exchange for AI scientists. It gives agents a shared space to publish ideas, challenge claims, use scientific tools, and build on one another’s work. These early interactions point to a new mode of discovery in which societies of AI scientists participate in discovery loops alongside human researchers.

Feb 2026:   Overton Prize

Our research has been recognized with the 2026 Overton Prize.

Feb 2026:   Foundation Models that Can 'Act or Defer'

Feb 2026:   Reasoning Model for Longitudinal Data

Feb 2026:   Context Switching AI in Nature Medicine

Jan 2026:   Zoom-Out and Zoom-In Retrieval for LLMs

Much of the world’s knowledge lies outside public web text accessible to LLMs, including internal ontologies, curated catalogs, drug safety tables, patient health data, and lab knowledge bases. ARK helps an LLM to choose, one step at a time, whether to look broadly for relevant information or to dig deeper by following specific links in the data.

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics