Computational Biology & Machine Learning Scientist

Computational Scientist – Machine Learning & Immunology & Biologics

A cutting-edge biotech organization is seeking highly motivatedComputational Scientiststo support the mission of decoding and engineering the immune system. The role focuses on developing advancedmachine learning and statistical modelsto analyze complex biological data, particularly immune repertoires and multimodal datasets.

About the Role

As part of a collaborative Computational Biology team, you will:

• Design and implement machine learning models—particularlylanguage models, diffusion models, or graph neural networks—tailored to biomedical challenges.
• Build novel computational methods for interpretingbiological sequences and structural data.
• Customize existing tools and develop new ones for integrative analysis and visualization oflarge-scale systems immunology data.
• Drive ML-based pipelines fordiagnostic or therapeutic design.
• Benchmark computational methods and optimize performance across datasets.
• Lead or contribute tocollaborative projectsspanning academic, clinical, and industry domains.

Required Qualifications

• PhD (or MSc with equivalent experience) inComputational Biology, Bioinformatics, Computer Science, Statistics, Physics, or related quantitative discipline.
• Strong background inmachine learning and statistical modeling, with a demonstrated ability to solve complex biological problems.
• Proven track record of scientific productivity (e.g., peer-reviewed publications).
• Hands-on experience indata handling, visualization, and biological data analysis.
• Proficient inPython, familiar withsoftware development best practices.
• Practical experience withTensorFlowand/orPyTorch.

Preferred Qualifications

• 3+ years post-graduate experience in academia or biotech/pharma, applyingML/AI to biological datasets.
• Prior exposure toimmunology, especiallyTCR/BCR repertoire analysis, or experience with protein design & or biologics.
• Deep expertise in at least one of the following areas:
• Language modelsfor sequence analysis
• Diffusion modelsin molecular design
• Graph MLin biomedical networks
• Experience withGPU computing (cloud or HPC clusters).