Sr. Research Scientist, Machine Learning Biological Foundation Models

Insilico Search Partners; Massachusetts; On-site; USD 150,000 - 230,000; Full time

Job summary

Insilico Search Partners seeks a Senior/Staff Machine Learning Scientist to lead the core AI research team focused on biological foundation models. You will architect novel DL approaches and train models on large-scale omics data to learn the language of genome biology.

You will collaborate with computational biologists and drug developers to inject biological priors into model design, evaluate scientific validity, and push frontier AI for human health.

Qualifications

• PhD (or equivalent) in Computational Biology, ML, CS, or related field.
• 2+ years post-graduate experience in genomics AI R&D.
• Experience building foundation models (CNNs, Transformers, sequence models).
• Proven ability to train and debug large-scale DL models with PyTorch.
• Experience with large genomic datasets and single-cell data.

Responsibilities

• Lead the creative research, architecture design, and training of biological foundation models on genomic, transcriptomic, and single-cell datasets.
• Collaborate with computational biologists and drug developers to integrate biological priors into model architectures and objectives.
• Rigorously implement, train, debug, and evaluate large-scale models to demonstrate scientific validity.
• Stay current with ML and computational biology advances and identify cross-disciplinary applications.

Skills

PhD equivalent

Genomics AI

Deep learning

PyTorch

Foundation models

Education

PhD in Computational Biology

Tools

PyTorch

Job description

Our client is a venture-backed biotechnology company applying artificial intelligence to transform drug discovery. Its proprietary AI platform decodes the complexity of molecular and genomic biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. Its multidisciplinary team spans machine learning, bioinformatics, data science, engineering, and drug development, and is reshaping how new medicines are created.

We are seeking an exceptional and creative Senior/Staff Machine Learning Scientist to lead and innovate within the core AI research team, focused on the creative building of biological foundation models. You will pioneer novel deep learning architectures and pre-training paradigms that learn the fundamental language of the genome and cellular biology. Rather than just applying out-of-the-box ML to biological datasets, you will design the next generation of foundation models tackling complex -omics data at scale. If you are a first-principles thinker excited to bridge advanced ML with genome biology to solve high-impact, frontier problems in human health and drug discovery, this is a unique opportunity.

Key Responsibilities

• Lead the creative research, architecture design, and training of biological foundation models on massive-scale genomic, transcriptomic, and single-cell datasets.
• Collaborate closely with computational biologists and drug developers to integrate deep biological priors directly into model architectures and training objectives, ensuring models capture fundamental and scientifically meaningful representations.
• Rigorously implement, train, debug, and evaluate large-scale models to demonstrate scientific validity and drive progress on frontier problems in human health and genetic medicines.
• Stay current with advancements in machine learning and computational biology research, identifying cross-disciplinary applications to solve real-world challenges.

Qualifications

• PhD (or evidence of equivalent level of expertise) with a strongly distinguished research focus in Computational Biology, Machine Learning, Computer Science, or a related quantitative field.
• 2+ years of relevant post-graduate experience at a leading industrial R&D lab or in a highly competitive academic environment building genomics AI.
• Deep understanding of modern deep learning and the creative building of foundation models, including CNNs, Transformers, and related sequence models (e.g., state-space models) specifically tailored for biological or genomic sequence data.
• A demonstrated track record of building and scaling AI models for complex biological datasets (e.g., single-cell genomics, DNA/RNA sequences) from initial conception to production.
• Proven ability to implement, train, and debug highly-performant deep learning models using frameworks like PyTorch.
• Experience working with massive datasets and a deep understanding of the engineering and algorithmic challenges associated with scale.