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Scientific Machine Learning Jobs in Boston, MA (NOW HIRING)

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Support scientific diligence with pharma and clinical partners * Contribute to Nucs AI\'s scientific credibility in the field What You Bring * PhD in machine learning, computer vision, medical image ...

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... Work closely with data scientists, clinicians, and software engineers to understand requirements ...

Senior Machine Learning Engineer

Boston, MA · Remote

$125K - $165K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... Work closely with data scientists, clinicians, and software engineers to understand requirements ...

They are seeking a highly motivated, product-oriented Machine Learning Scientist to join their core R&D team, where the role involves designing and deploying ML models for indoor positioning and ...

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Scientific Machine Learning information

See Boston, MA salary details

$15

$34

$56

How much do scientific machine learning jobs pay per hour?

As of Jun 27, 2026, the average hourly pay for scientific machine learning in Boston, MA is $34.20, according to ZipRecruiter salary data. Most workers in this role earn between $20.91 and $43.61 per hour, depending on experience, location, and employer.

Is ML a high paying job?

Scientific Machine Learning roles typically offer high salaries due to the specialized skills required, such as expertise in deep learning, data analysis, and programming with tools like Python and TensorFlow. Compensation varies by industry, experience, and location but generally exceeds average tech salaries for comparable roles.

Which 3 jobs will survive AI?

Scientific Machine Learning professionals will likely continue to be in demand due to their expertise in developing and applying AI models to complex scientific problems. Roles such as data scientists, AI researchers, and machine learning engineers are expected to persist because they require specialized knowledge, critical thinking, and ongoing innovation that AI tools complement rather than replace. These jobs often involve interdisciplinary skills, programming, and understanding of domain-specific data, making them more resilient to automation.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

How much does a machine learning scientist make?

A machine learning scientist typically earns between $90,000 and $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in deep learning or natural language processing can earn higher salaries, often exceeding $180,000.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

Is 40 too late for data science?

Scientific Machine Learning roles often value skills and experience over age, and many professionals transition into data science or machine learning at various stages of their careers. Learning relevant tools like Python, TensorFlow, or scikit-learn and gaining practical experience can help regardless of age, making 40 not too late to pursue this field.
What are popular job titles related to Scientific Machine Learning jobs in Boston, MA? For Scientific Machine Learning jobs in Boston, MA, the most frequently searched job titles are:
What cities near Boston, MA are hiring for Scientific Machine Learning jobs? Cities near Boston, MA with the most Scientific Machine Learning job openings:
Infographic showing various Scientific Machine Learning job openings in Boston, MA as of June 2026, with employment types broken down into 3% As Needed, 55% Full Time, 23% Part Time, and 19% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $71,130 per year, or $34.2 per hour.
(Senior) Scientist, Machine Learning

(Senior) Scientist, Machine Learning

Flagship Pioneering, Inc.

Cambridge, MA • On-site

$100K - $136K/yr

Full-time

Medical, Retirement

Posted 9 days ago


Job description

What if... you could join an organization that creates, resources, and builds life sciences companies that invent breakthrough technologies in order to transform health care and sustainability?
COMPANY DESCRIPTION
Expedition Medicines is a privately held, early-stage biotechnology company pioneering the emerging field of Protein Editing. At Expedition Medicines we create small molecules that edit protein structure and function to unlock presently undruggable targets and a broad array of therapeutic modalities. Our platform integrates novel small molecule chemistry and chemoproteomic discovery technologies with Machine Learning (ML) to enable generative design. Expedition Medicines is backed by Flagship Pioneering, bringing their courage, vision, and resources to guide Expedition Medicines from platform validation to patient impact. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!
THE ROLE
Expedition is seeking a motivated and innovative (Senior) Scientist, Machine Learning to join our team. In this role, you will play a critical role in developing, evaluating, and applying machine learning approaches that connect Expedition's proprietary chemoproteomics data with quantum chemistry, electronic structure, and generative molecular design. The successful candidate will combine strong expertise in modern machine learning with a deep understanding of molecular representation, quantum chemistry, and computational drug discovery to drive impact across Expedition's drug discovery programs.
This individual will contribute to the advancement of a state-of-the-art AI platform for covalent drug discovery, with a focus on linking large-scale atom-precision experimental data to physically meaningful features such as electronic structure, reactivity, and DFT-derived descriptors. A key part of the role will be applying generative models directly to active drug discovery programs in close partnership with medicinal chemistry, while developing rigorous benchmarks to evaluate model performance and guide platform improvement. The ideal candidate is highly collaborative, scientifically rigorous, comfortable with hands-on data curation, and capable of independently driving projects in a fast-paced research environment.
KEY RESPONSIBILITIES
  • Develop, implement, and evaluate innovative machine learning methods for connecting (macro-)molecular quantum chemical features, reactivity modeling, covalent bond formation, and proteome-wide target engagement data
  • Design and implement rigorous benchmarks to evaluate model performance, including retrospective, prospective, and program-relevant validation strategies
  • Refine, fine-tune and apply Expedition's foundational models to our drug discovery programs in close partnership with medicinal chemistry teams, supporting compound design, prioritization, and iterative learning from experimental results
  • Perform hands-on data curation, quality control, and dataset construction to ensure that models are trained and evaluated on high-quality, biologically and chemically meaningful data
  • Develop scalable featurization and modeling pipelines for large molecular datasets, including quantum chemistry outputs, conformer ensembles, protein-ligand interaction data, covalent reactivity data, and experimental chemoproteomics data
  • Collaborate closely with computational, chemistry, biology, and proteomics teams to translate platform data into actionable models for discovery programs
  • Partner with engineering teams to productionize modeling workflows, improve data infrastructure, and build self-serve capabilities for chemistry and discovery teams
  • Communicate technical findings, model performance, and scientific implications clearly across cross-functional teams

PROFESSIONAL EXPERIENCE & QUALIFICATIONS
  • Ph.D. in machine learning, computational chemistry, chemical physics, computer science, applied mathematics, or a related discipline with 2+ years of industry experience, or M.S. degree with 6+ years of industry experience
  • Experience with quantum chemistry, DFT, electronic structure methods, or post-DFT descriptors.
  • Experience building molecular ML models including graph neural networks, geometric deep learning, equivariant architectures, diffusion models, or related approaches. Publications or preprints in, e.g., NeurIPS, ICML, ICLR, bioRxiv a strong plus.
  • Experience applying generative models, molecular design models, or active learning workflows to drug discovery or chemistry optimization problems
  • Experience working closely with medicinal chemistry teams to prioritize compounds, interpret model outputs, and incorporate experimental feedback into model development
  • Experience developing rigorous model evaluation frameworks, benchmarks, and validation strategies for molecular ML or scientific machine learning applications and an ability to curate, clean, integrate, and analyze complex, large-scale scientific datasets from multiple sources
  • Proficiency with Python and modern ML frameworks such as PyTorch, PyTorch Geometric, DGL, or related tools and cheminformatics and molecular modeling toolkits such as RDKit, ORCA, Gaussian, Q-Chem, or related software is preferred
  • Experience with scalable data processing, model training, and analysis workflows for large scientific datasets
  • Experience with covalent chemistry, reaction modeling, structure-based design, or chemoproteomics data is a plus
  • Ability to work closely with experimental scientists and translate biological and chemical questions into computational strategies
  • Excellent communication and cross-functional collaboration skills

LOCATION: Cambridge, MA
ABOUT FLAGSHIP PIONEERING
Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity's most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.
Flagship has been recognized twice on FORTUNE's "Change the World" list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company's annual list of the World's Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.
At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.
We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.
Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, "FSP") do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.
#LI-MB1
The salary range for this role is $132,000 - $258,500. Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. Expedition Medicines currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Expedition Medicines's good faith estimate as of the date of publication and may be modified in the future.
Privacy Notice for Applicants: When you apply for a role at Flagship Pioneering or one of its portfolio companies, we collect and use personal information you provide (such as your name, contact details, work history, and application materials) to evaluate your application, communicate with you, and comply with legal obligations. Your application data is processed through Greenhouse, our applicant tracking system, and may also be reviewed using AI-assisted screening tools. We do not sell your personal information. California residents have rights under the CCPA/CPRA including to know, delete, and opt out of the sharing of their personal information. If you are located in the EU or UK, we process your data under GDPR and you have rights to access, rectify, and erase your data. To exercise your rights or for questions, contact privacy@flagshippioneering.com.