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Machine Learning Biology Jobs in Massachusetts (NOW HIRING)

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How much do machine learning biology jobs pay per year?

As of Sep 6, 2026, the average yearly pay for machine learning biology in Massachusetts is $56,998.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,100.00 and $66,100.00 per year, depending on experience, location, and employer.

What is a machine learning biology?

A Machine Learning Biology job involves applying machine learning techniques to analyze biological data, such as genomic sequences, protein structures, or medical images. Professionals in this field develop algorithms to identify patterns, make predictions, and derive insights that can advance research in drug discovery, personalized medicine, and biotechnology. These roles typically require expertise in biology, data science, and programming, often using tools like Python, TensorFlow, or scikit-learn.

What are the key skills and qualifications needed to thrive in machine learning biology?

To thrive as a Machine Learning Biology professional, you need expertise in both computational methods (especially machine learning and data science) and a solid understanding of biological sciences, typically supported by an advanced degree in bioinformatics, computational biology, or a related field. Familiarity with programming languages like Python or R, experience using machine learning frameworks (such as TensorFlow or scikit-learn), and working with biological databases are highly valued. Strong analytical thinking, problem-solving abilities, and effective interdisciplinary communication are key soft skills for this position. These competencies are vital for translating complex biological data into actionable insights and advancing research or product development in biotechnology and life sciences.

What are some common challenges faced by professionals working in machine learning biology?

Professionals in Machine Learning Biology often deal with challenges such as handling large and complex biological datasets, integrating heterogeneous data types (like genomics, proteomics, or imaging), and addressing the noise and variability inherent in biological data. Interpreting results in a biologically meaningful way and ensuring reproducibility of models can also be complex, requiring close collaboration with experimental scientists. Many teams are cross-functional, so frequent communication with biologists, clinicians, and software engineers is important for project success. While these challenges can be demanding, they also offer opportunities for innovation and significant contributions to scientific discovery or medical advances.

What are the most commonly searched types of Machine Learning Biology jobs in Massachusetts?

The most popular types of Machine Learning Biology jobs in Massachusetts are:

What are popular job titles related to Machine Learning Biology jobs in Massachusetts?

For Machine Learning Biology jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Machine Learning Biology jobs in Massachusetts look for?

The top searched job categories for Machine Learning Biology jobs in Massachusetts are:

What cities in Massachusetts are hiring for Machine Learning Biology jobs?

Cities in Massachusetts with the most Machine Learning Biology job openings:

Machine Learning Biology jobs near you
Infographic showing various Machine Learning Biology job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $56,998 per year, or $27.4 per hour.

Computational Biology & Bioinformatics Lead

Institute for Protein Innovation

Boston, MA • On-site

$200K - $240K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 15 days ago

Job description

The Institute for Protein Innovation (IPI)is a nonprofit research organization advancing protein science to accelerate research and improve human health. Founded in 2017 and located in Boston's Longwood Medical Area, the Institute's three-pronged strategy is to build protein tools, conduct related internal research and develop educational programs for the protein science and biological research communities.
With a significant endowment, IPI uniquely combines the freedom of academia with the high throughput and scale of industry to take on transformative projects. IPI has built a robust platform for discovering, developing, and distributing synthetic antibodies and other protein tools to share with the biomedical community. The Institute's deep protein expertise, collaborative spirit and research tools are powering new biomedical and therapeutic discoveries with a growing community of researchers at Harvard Medical School, Boston Children's Hospital and other institutions across Greater Boston and beyond.
Purpose
Computational Biology & Bioinformatics Lead will help grow IPI's computational protein science team and be responsible for the machine learning and data systems that support work across the Institute. This includes the following functional teams: antibody and antigen discovery, protein characterization, neuroscience, lab automation and lab operations.
This role is responsible for training and applying foundation models for protein structure prediction and design, building models that predict biophysical properties from sequence and structure and turning large multimodal biological datasets into tools and portals that can be effectively utilized across the Institute.
This position provides a combination of hands-on technical work with team leadership skills to fulfill responsibilities and achieve goals.
The position will collaborate closely with the Associate Director & Program Manager of the Antibody Platform and reports to the Senior Director of the Antibody Platform.
Primary Responsibilities
  1. Train, fine-tune, and benchmark foundation models for protein folding and design, including structure prediction models and generative models for de novo binder and antibody design, and integrate these models for routine use in antigen and antibody discovery projects.
  2. Build and validate models that predict biophysical properties such as stability, aggregation, expression, binding affinity, and developability, using multimodal data across sequence, structure, next generation sequencing, proteomics, and assay results.
  3. Run in silico binder and antibody design campaigns and pair them with experimental rounds so predictions are tested and the results feed back into the models.
  4. Develop pipelines and platforms for in vitro antibody discovery data, protein biophysical characterization, proteomics, and next generation sequencing analysis.
  5. Build and maintain web portals and databases for large biological datasets, including IPI's external antigen and antibody catalogs (for example OpenAntigens) and internal research databases.
  6. Work with teams and groups across the Institute to design experiments, interpret and analyze results, and effectively integrate computational tools into established team workflows.
  7. Manage cloud and high-performance computing environments, including GPU infrastructure for model training and large-scale analysis.
  8. Lead and mentor a small team of computational biologists and bioinformaticians.
  9. Effectively present computational analyses to technical and non-technical audiences and contribute to publications, patents, and products.
  10. Establish standards for data management, version control, reproducibility, and MLOps in the group.
Qualifications
Required
  • PhD in computational biology, bioinformatics, biophysics, machine learning, or a related field, with a strong background in protein science or biochemistry.
  • 5 or more years of relevant experience, including experience leading or mentoring computational staff or serving as a technical lead. Direct experience managing a team of two to four people is a plus.
  • Strong Python and hands-on experience with deep learning frameworks such as PyTorch.
  • Experience developing, modifying, and applying protein machine learning models for structure prediction and design.
  • Experience building or training models on multimodal biological data, with a track record shown through publications, patents, or products.
  • Experience with de novo protein and antibody design and validation cycles.

Preferred. (strong candidates will bring several of these, but not all are required)
  • Experience with next generation sequencing analysis.
  • Experience building data portals, APIs, or databases for large biological datasets.
  • Experience with cloud computing and high-performance computing or GPU environments.
  • Familiarity with antibody discovery, proteomics, or protein biophysical characterization assays.
  • Experience integrating computational predictions with wet-lab workflows, including LIMS or ELN systems.

$200,000 - $240,000 a year
IPI provides competitive compensation and an excellent benefits package to support physical, mental and financial health. Highlights of benefits include:
• 100% employer-paid medical, dental, and vision plans
• Flexible spending accounts and a healthcare reimbursement account
• 401(k) plan with generous 6% employer match - immediately 100% vested
• Generous PTO package
• Commuter and parking reimbursement
• Career development opportunities
For more information, visit proteininnovation.org or follow us on social media, @ipiproteins. IPI is an independent 501(c)(3) nonprofit research organization and an equal-opportunity employer. The Institute celebrates diversity and is committed to creating an inclusive environment for all employees. Please be advised that you will be required to provide evidence of your identity and eligibility for employment in the United States. IPI will not guarantee sponsorship of foreign nationals and retains complete discretion regarding providing sponsorship to any prospective or existing employee at time of hire or at any time in the future.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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