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Machine Learning Computational Biology Jobs (NOW HIRING)

Verge Genomics

Computational Biologist

San Francisco, CA · On-site +1

... machine learning objectives. Requirements Candidates must have : * Either: * PhD in computational biology, AI/ML, applied statistics, biophysics, or , * MS and professional experience in relevant ...

Neptune Bio

Computational Biologist

New York, NY

Stay current with advances in computational biology, machine learning, and scalable infrastructure, applying them to ongoing research challenges. * Communicate findings clearly through reports ...

Neptune Bio

Computational Biologist

New York, NY · On-site

Stay current with advances in computational biology, machine learning, and scalable infrastructure, applying them to ongoing research challenges. * Communicate findings clearly through reports ...

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Experience with cheminformatics, computational chemistry, computational biology databases, data ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Experience with cheminformatics, computational chemistry, computational biology databases, data ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

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Machine Learning Computational Biology information

What types of projects might I work on as a Machine Learning Computational Biology specialist?

As a Machine Learning Computational Biology specialist, you may work on projects ranging from analyzing large-scale genomic data to developing predictive models for disease risk or drug response. Typical tasks include designing and implementing machine learning algorithms to identify patterns in biological datasets, collaborating with biologists and clinicians to interpret results, and contributing to publications or presentations. You'll often be part of a multidisciplinary team, interacting with data scientists, laboratory researchers, and software engineers. This role offers the opportunity to work on cutting-edge biomedical research and have a direct impact on advancements in healthcare and life sciences.

What is a Machine Learning Computational Biology job?

A Machine Learning Computational Biology job involves applying machine learning techniques to analyze biological data, such as genomics, proteomics, and medical imaging. Professionals in this field develop algorithms and models to identify patterns, make predictions, and generate insights that can drive scientific discovery or improve healthcare. They typically work with large datasets, employing statistical and computational methods to solve complex biological problems. The role often requires expertise in programming, data science, and domain-specific biological knowledge. It is commonly found in academia, pharmaceutical companies, biotech firms, and healthcare institutions.

What are the key skills and qualifications needed to thrive in the Machine Learning Computational Biology position, and why are they important?

To thrive as a Machine Learning Computational Biology professional, you need a strong background in biology, statistics, computer science, and machine learning, typically supported by an advanced degree in a relevant field. Familiarity with programming languages such as Python or R, experience with bioinformatics tools, and knowledge of machine learning frameworks like TensorFlow or scikit-learn are commonly required. Strong analytical thinking, effective communication, and the ability to work collaboratively in interdisciplinary teams are highly valued soft skills. These qualifications are essential for solving complex biological problems, developing robust computational models, and effectively communicating findings to both technical and non-technical stakeholders.

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Machine Learning Computational Biology jobs near you
Infographic showing various Machine Learning Computational Biology job openings in the United States as of June 2026, with employment types broken down into 76% Full Time, and 24% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.
Computational Biologist

Computational Biologist

Verge Genomics

San Francisco, CA • On-site, Remote

Full-time

Posted 27 days ago

Key responsibilities

  • Develop and evaluate computational methodologies integrating multi-omic datasets to create predictive models for translational biology.

  • Lead projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration.

  • Lead partnerships with AI companies to co-develop foundation models for drug discovery.

Job description

Who We Are
Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field's largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.
Your Mission
Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Computational Biologist (AI/ML) will be responsible for defining and enabling new product offerings leveraging Verge's drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers.
Your 12 Month Outcomes
  • Work with Verge's AI partners to deliver a best-in-class biology foundation model with Verge's proprietary datasets
  • Develop a novel approach that enables a powerful new product offering (patient stratification, biomarker discovery, etc.)
  • Deliver at least two CONVERGE-powered insights projects to pharma/biotech companies
  • Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration

You Will
  • Develop and evaluate cutting-edge computational methodologies integrating multi-omic datasets to develop predictive models for translational biology,
  • Lead high-impact projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration,
  • Lead partnerships with AI companies to co-develop next-generation foundation models for drug discovery
  • Frame biological problems in computational terms and design solutions that are biologically meaningful, interpretable, and experimentally testable,
  • Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications,
  • Translate between biological domain knowledge and machine learning objectives.

Requirements
Candidates must have:
  • Either:
    • PhD in computational biology, AI/ML, applied statistics, biophysics, or,
    • MS and professional experience in relevant fields.
  • ≥5 years of experience working in applied computational biology and integration of multi-omic datasets (RNA-seq, genotyping, clinical), with ≥2 years in a startup environment,
  • ≥2 years of experience in relevant areas of translational science, demonstrating a deep understanding of target identification, biomarker discovery, and/or patient stratification,
  • Proven ability to implement, evaluate, and/or create computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery,
  • Fluency with state of the art in systems biology workflows, including off-the-shelf biological databases and computational biology tools,
  • Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
  • Track record of individual innovation, with published research or shipped work influencing pharma R&D decisions
  • Experience running a significant number of end-to-end RNA-Seq data analyses (from QC, read quantification, normalization through to interpretation),
  • Excellent coding skills in Python, with experience in relevant ML/AI libraries (e.g., PyTorch, HuggingFace, scikit-learn, pandas, numpy). A demonstrable portfolio (e.g., GitHub, research code, or shared notebooks) is highly preferred,
  • Experience in building and evaluating machine learning models on biological data, ideally with transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a deep understanding of feature selection, model interpretability,
  • Professional experience with AI workflows, including natural language processing (NLP), retrieval-augmented generation (RAG), embeddings, vectorization of diverse data types, and working with large language models (e.g., GPT),
  • Demonstrated experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.

Finally, we seek candidates who embrace our values and way of working:
  • Ability to thrive in uncertainty with frequently changing priorities
  • Deep alignment with our values
  • A passion for making an impact on patients

Apply