Associate Scientist, Postdoctoral Research Fellow, AI-Guided Functional Immunology

Job Description

Our AI/ML team develops biologically grounded and interpretable machine learning methods to accelerate target discovery across immunology. We are seeking a highly motivated Postdoctoral Research Fellow to develop next-generation perturbation modeling approaches that integrate single-cell transcriptomics, functional genomics, and human organoid models to elucidate immunological disease mechanisms and generate experimentally validated biological insightsf.

What You’ll Do:

• Advance AI/ML methods that connect single-cell foundation models with functional perturbation data.
• Develop interpretable and reproducible computational approaches to predict how genetic and chemical perturbation of immune cells reshape cell states.
• Design and interpret perturbation designs together with experimental groups and feed the resulting data back into the models.
• Develop experiment-selection strategies (active learning, Bayesian optimization) that decide which knockouts the next round tests and characterize how a model-chosen training set biases its own evaluation.
• Design evaluations for out-of-distribution generalization held-out cell contexts, matched-decoy nulls, and the linear and mean baselines a foundation model must beat to earn trust.
• Collaborate with multidisciplinary teams across data science, immunology, functional genomics, and translational research.
• Communicate scientific findings through presentations, publications, and cross-functional discussions.

You Should Have:

• Strong expertise in machine learning, deep learning, and data science.
• Demonstrated research productivity through publications, preprints, or conference presentations.
• Ability to communicate complex technical concepts to multidisciplinary scientific teams.

Educational Requirement:

• Must currently hold a PhD OR
• Receive a Ph.D. no later than spring 2027
• Ph.D. in Computational Biology, Bioinformatics, AI/ML, Computer Science, Immunology, Bioengineering, Statistics, or a related quantitative discipline

Required Experience

Skills:

• Experience with single-cell omics analysis, representation learning, or biomedical data analysis.
• Strong expertise in machine learning, deep learning, and data science.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Experience applying and evaluating deep models on high-dimensional biological data (single-cell, imaging, or similar).
• Proficiency in Foundation Models, Perturbation Modeling, and/or Organoid Models.
• Experience with CRISPR and target discovery.

Preferred

Skills and Experience:

• Single-cell foundation models, transformers, and representation learning.
• Genetic perturbation modeling: CRISPR screens, Perturb-seq, or arrayed knockouts.
• Wet-lab experience, especially involving organoid, iPSC, or immune co-culture model systems.
• Background understanding of, or experience working in, immunology.
• Experimental design, active learning, or model benchmarking and evaluation.
• Publications in venues such as NeurIPS, ICML, ISMB/RECOMB, Cell Systems, Nature Methods, or related journals.

The salary range for this role is: $82,000-$92,000

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.

The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.

We offer a comprehensive package of benefits.

Available benefits include medical, dental, vision healthcare and other insurance benefits (for…