Associate Researcher I- Pharmacological Sciences

Mount Sinai Health System • New York, NY

6d • Onsite

About The Position

The Associate Researcher I is a laboratory research position, responsible for independent conduct of routine and standardized experiments. This individual assists in the interpretation of research outcomes, performs assays to support research studies, and assists in the clerical and supply aspects of the laboratory environment. The Associate Researcher I will develop novel AI and machine learning models for antibody and nanobody design, structure prediction, epitope targeting, and in silico affinity maturation. The project integrates large-scale antibody repertoire analysis, structure-informed modeling, and generative design to enable the discovery and optimization of high-affinity, multi-epitope targeting IgG and VHH antibodies for therapeutic applications.

Requirements

• Bachelors or Masters degree in science or related field required
• No experience required.
• One year of research experience preferred.

Nice To Haves

• Master’s degree in computational biology, bioinformatics, computer science, biomedical engineering, or a related field.
• Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
• Knowledge of protein structure, antibody engineering, and structural bioinformatics.
• Experience with Linux-based computing environments, HPC and version control systems.
• Ability to work independently and collaboratively in a multidisciplinary research environment.

Responsibilities

• Develop, train, benchmark, and deploy deep learning models for antibody and nanobody structure prediction, inverse folding, affinity prediction, and generative sequence design.
• Build large-scale antibody–antigen datasets from public and in-house repertoire, structural, and biophysical data.
• Implement pipelines for epitope mapping, paratope analysis, and multi-epitope antibody design.
• Perform in silico affinity maturation using structure-guided and language-model-based approaches.
• Design and maintain reproducible data processing, model training, and evaluation pipelines using Python, PyTorch, and related ML frameworks.
• Collaborate with experimental scientists to prioritize, interpret, and iterate on designed antibody candidates.
• Analyze model outputs using structural modeling, docking, and confidence scoring metrics.
• Maintain detailed documentation of computational workflows and results.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 5,001-10,000 employees