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

MSD

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Hybrid

Quick summary

Work type: Hybrid
Location: Cambridge, MA
Salary: $82,000–$92,000 / yr
Employment: Full-time
Posted: 2 days ago
Freshness: Confirmed live today
Closes: Oct 19, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $220k

This role $87k

$59k $292k

below market most similar roles pay here above market

This role pays less than 98% of similar roles. Most pay $184,602–$254,750 — the blue band above. At the midpoint, this role pays about $87k versus about $220k for comparable roles.

Based on 240 similar postings.

Employer

About MSD

MSD (Merck Sharp & Dohme) is the international name for Merck & Co., a major U.S.-based pharmaceutical company.

MSD currently has 11 open roles on FindRole.

Listed pay typically runs $82,000–$92,000 across 11 roles with salary data.

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At a glance

TL;DR · Associate Scientist, Postdoctoral Research Fellow, AI-Guided Functional Immunology

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JOB TITLE: Associate Scientist, Postdoctoral Research Fellow, AI-Guided Functional Immunology The AI/ML team is seeking a Postdoctoral Research Fellow to develop next-generation perturbation modeling approaches that integrate single-cell transcriptomics, functional genomics, and human organoid models. The successful candidate will advance AI/ML methods connecting single-cell foundation models with functional perturbation data to predict how genetic and chemical perturbations reshape immune cell states. Daily responsibilities include designing experiment-selection strategies using active learning and Bayesian optimization, interpreting perturbation designs with experimental groups, and developing reproducible computational approaches for out-of-distribution generalization. Required skills include expertise in deep learning, Python, PyTorch, and single-cell omics analysis. The role addresses the technical challenge of elucidating immunological disease mechanisms and accelerating target discovery by creating interpretable, biologically grounded machine learning models for high-dimensional biological data, including CRISPR screens and Perturb-seq.

Skills

Python PyTorch Deep Learning Machine Learning Foundation Models Single-cell Transcriptomics Representation Learning Transformers Active Learning Bayesian Optimization CRISPR Perturbation Modeling Statistical Analysis Design of Experiments Bioinformatics Image Analysis

What you'll do

Develop AI/ML methods to connect single-cell foundation models with functional perturbation data.
Create interpretable computational approaches to predict how genetic and chemical perturbations reshape immune cell states.
Design and interpret experimental perturbation designs in coordination with experimental research groups.
Develop experiment-selection strategies using active learning and Bayesian optimization to identify next-round knockouts.
Design evaluations for out-of-distribution generalization and establish benchmarks for foundation model performance.
Communicate scientific findings through presentations, publications, and cross-functional discussions.

What we're looking for

Must currently hold a PhD or receive a PhD no later than spring 2027.
PhD must be in Computational Biology, Bioinformatics, AI/ML, Computer Science, Immunology, Bioengineering, Statistics, or a related quantitative discipline.
Must have strong expertise in machine learning, deep learning, and data science.
Must have experience with single-cell omics analysis, representation learning, or biomedical data analysis.
Must be proficient in Python and deep learning frameworks such as PyTorch.
Must have experience applying and evaluating deep models on high-dimensional biological data.
Must have experience with CRISPR and target discovery.
Experience with single-cell foundation models, transformers, and genetic perturbation modeling is preferred (preferred).

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