Machine Learning Scientist Jobs in Massachusetts
Machine Learning Scientist jobs in Massachusetts are among the most active in the country, concentrated in biotech and life sciences, financial technology, defense and aerospace, and enterprise software, with openings at every level from junior researcher through principal scientist. The heaviest hiring is in Greater Boston, Cambridge, and the Route 128 corridor, where employers like Biogen, MathWorks, and Raytheon Technologies maintain deep machine learning practices. The most in-demand specialties right now are natural language processing, computer vision, and reinforcement learning applied to drug discovery and autonomous systems. Find a role that fits below and apply directly.
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Showing 5 of 14+ Machine Learning Scientist jobs
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team builds models and systems that take a biologic from design specification to wet-lab validated lead.
We're hiring a machine learning scientist to design molecules on real programs and turn what they learn into capabilities that generalize across programs. The ideal candidate is an exceptional builder with strong biological intuition: someone who can turn work on individual campaigns into reliable, extensible systems that improve how we design molecules across programs. You'll work closely with domain scientists, platform teams, and AI researchers to connect specialist protein design models to Lila's broader autonomous science platform.
What You'll Be Building
- Design molecules for active biologics programs, partnering with domain scientists to translate target, mechanism, and experimental constraints into actionable design hypotheses.
- Develop reasoning capabilities for drug discovery, including orchestrating design workflows.
- Design and maintain benchmarks and evaluation infrastructure that measure whether design workflows produce useful, generalizable decisions across biologics programs.
- Own operations around reproducibility, throughput, and inference cost of computational design workflows.
- Work with domain scientists to understand how designs are prioritized and turn that judgment into ML objectives and evaluation criteria.
What You'll Need to Succeed
- MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or a similar quantitative field.
- Strong software engineering and system design fundamentals.
- Rigor in evaluation and dataset design: how benchmarks leak, why a good validation number fails downstream, and how to measure whether an automated system is making good decisions.
- Strong cross-functional communication skills.
- Domain expertise in protein sequence, structure, and function.
Bonus Points For
- Experience building reasoning models, agents, planning systems, or multi-step ML orchestration.
- Exposure to designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn loops.
- Experience in developing evaluation harnesses, model registries, or benchmark suites.
- Training or serving models at scale: distributed training, GPU efficiency, high-throughput inference.
- Publications, open-source contributions, or applied research outputs in AI for Science venues.
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team builds models and systems that take a biologic from design specification to wet-lab validated lead.
We're hiring a machine learning scientist to design molecules on real programs and turn what they learn into capabilities that generalize across programs. The ideal candidate is an exceptional builder with strong biological intuition: someone who can turn work on individual campaigns into reliable, extensible systems that improve how we design molecules across programs. You'll work closely with domain scientists, platform teams, and AI researchers to connect specialist protein design models to Lila's broader autonomous science platform.
What You'll Be Building
- Design molecules for active biologics programs, partnering with domain scientists to translate target, mechanism, and experimental constraints into actionable design hypotheses.
- Develop reasoning capabilities for drug discovery, including orchestrating design workflows.
- Design and maintain benchmarks and evaluation infrastructure that measure whether design workflows produce useful, generalizable decisions across biologics programs.
- Own operations around reproducibility, throughput, and inference cost of computational design workflows.
- Work with domain scientists to understand how designs are prioritized and turn that judgment into ML objectives and evaluation criteria.
What You'll Need to Succeed
- MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or a similar quantitative field.
- Strong software engineering and system design fundamentals.
- Rigor in evaluation and dataset design: how benchmarks leak, why a good validation number fails downstream, and how to measure whether an automated system is making good decisions.
- Strong cross-functional communication skills.
- Domain expertise in protein sequence, structure, and function.
Bonus Points For
- Experience building reasoning models, agents, planning systems, or multi-step ML orchestration.
- Exposure to designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn loops.
- Experience in developing evaluation harnesses, model registries, or benchmark suites.
- Training or serving models at scale: distributed training, GPU efficiency, high-throughput inference.
- Publications, open-source contributions, or applied research outputs in AI for Science venues.
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
See All 14 Machine Learning Scientist Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find JobsMachine Learning Scientist Jobs by City in Massachusetts
Where Massachusetts roles are concentrated, by current openings.
Machine Learning Scientist Job Market in Massachusetts
A snapshot from current Massachusetts openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- E-Commerce & Online Marketplaces
- Retail
- Banking & Financial Services
- Biotechnology & Pharmaceuticals
What Massachusetts Employers Look For
The qualifications that appear most often in machine learning scientist jobs across Massachusetts.
- PhD or master's degree in computer science, statistics, or a related quantitative field
- Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow
- Experience designing and deploying end-to-end ML models in production environments
- Strong background in statistical modeling, probability theory, and experimental design
- Familiarity with cloud infrastructure such as AWS, Azure, or Google Cloud for ML workloads
- Ability to communicate research findings clearly to both technical and non-technical stakeholders
Machine Learning Scientist Jobs in Massachusetts: Frequently Asked Questions
How do you become a machine learning scientist in Massachusetts?
Machine learning scientists in Massachusetts do not require a state-issued license, so the path runs through education and demonstrated research ability. Most roles require at least a master's degree, and many Boston-area employers, particularly in biotech and defense, expect a PhD with published research. Building a portfolio of end-to-end projects, contributing to open-source research, and targeting companies along the Route 128 corridor or in Kendall Square strengthens a candidate's position considerably.
How much do machine learning scientists make in Massachusetts?
Machine learning scientists in Massachusetts earn a median of about $131,750 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $81,030 for the lowest 10% to over $206,220 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire machine learning scientists in Massachusetts?
Employers hiring machine learning scientists in Massachusetts right now include Wayfair, Lila Sciences, and STR, based on current listings on Migrate Mate as of September 2026. Massachusetts's dense concentration of research universities and biotech firms means many openings combine applied ML with domain expertise in healthcare, robotics, or financial modeling.
Which Massachusetts cities have the most machine learning scientist jobs?
Boston, Cambridge, and Waltham have the most machine learning scientist openings in Massachusetts. Greater Boston and Cambridge dominate because of the Kendall Square biotech cluster, major research universities, and financial technology firms headquartered downtown, while suburban Route 128 cities draw openings from defense contractors and established enterprise software companies with large R&D campuses.
Are there remote machine learning scientist jobs in Massachusetts?
Yes, and more than most fields. About 20% of machine learning scientist openings tied to Massachusetts are remote or hybrid as of September 2026, reflecting how much of the work involves writing code, running experiments, and analyzing results rather than being physically on-site. Research-heavy roles at biotech and defense firms tend to require more in-person collaboration, while pure modeling and inference engineering roles are most often offered with flexible arrangements.
How can I get hired as a machine learning scientist in Massachusetts with little or no experience?
The most realistic entry path is a research assistant or associate ML engineer role at one of Massachusetts's many teaching hospitals, university-affiliated labs such as those connected to MIT or Harvard, or a biotech company running a structured new-graduate program. Building a GitHub portfolio of reproducible ML experiments, completing a relevant master's or doctoral program, and applying to rotational research programs at larger employers like Biogen or MathWorks gives candidates without industry experience a credible starting point.
Where can I find and apply to machine learning scientist jobs in Massachusetts?
You can find and apply to machine learning scientist jobs in Massachusetts on Migrate Mate, which lists current openings in the state. Find roles that fit your background and apply directly to the ones that interest you.
See All 14 Machine Learning Scientist Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
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