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Scientific Machine Learning Jobs in Massachusetts

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$124K - $149K/yr

What We're Looking For (Must-Haves): * BS or MS in Computer Science, Machine Learning, or a related field. * Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable ...

STR

Lead Research Scientist - Machine Learning

Woburn, MA · On-site

$174K - $220K/yr

... machine learning, statistics, estimation theory, and information theory algorithms for signals ... scientific field such as applied math, physics, electrical engineering, computer science, or data ...

STR

Lead Research Scientist - Machine Learning

Boston, MA

$174K - $220K/yr

... machine learning, statistics, estimation theory, and information theory algorithms for signals ... scientific field such as applied math, physics, electrical engineering, computer science, or data ...

Neurable, INC

Machine Learning

Cambridge, MA · On-site

We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would like to advance our current methods of identifying brain activity, using novel machine learning ...

Neurable, INC

Machine Learning

Cambridge, MA

We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would like to advance our current methods of identifying brain activity, using novel machine learning ...

Bracebridge Capital

Machine Learning Analyst

Boston, MA

$110K - $145K/yr

Proficiency in Python and familiarity with the Python data science stack (NumPy, SciPy, Pandas ... Familiarity with machine learning and statistical modeling fundamentals, including model evaluation ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

MS/PhD in Computer Science, Machine Learning, or related field. * Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning. * Background in autonomous ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

MS/PhD in Computer Science, Machine Learning, or related field. * Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning. * Background in autonomous ...

DraftKings

Senior Machine Learning Engineer

Boston, MA · On-site

$133K - $175K/yr

The Crown Is Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale ... The Fintech Data Science team builds and maintains systems that protect DraftKings and its ...

DraftKings, Inc.

Senior Machine Learning Engineer

Boston, MA · On-site

$133K - $175K/yr

The Crown Is Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale ... The Fintech Data Science team builds and maintains systems that protect DraftKings and its ...

Bracebridge Capital

Machine Learning Analyst

Boston, MA · On-site

$110K - $145K/yr

Proficiency in Python and familiarity with the Python data science stack (NumPy, SciPy, Pandas ... Familiarity with machine learning and statistical modeling fundamentals, including model evaluation ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Massachusetts

Scientific Machine Learning information

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.

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.

What are popular job titles related to Scientific Machine Learning jobs in Massachusetts? For Scientific Machine Learning jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Massachusetts look for? The top searched job categories for Scientific Machine Learning jobs in Massachusetts are:
What cities in Massachusetts are hiring for Scientific Machine Learning jobs? Cities in Massachusetts with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Massachusetts as of July 2026, with employment types broken down into 1% As Needed, 70% Full Time, 26% Part Time, 2% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Scientist, Machine Learning (Principal Scientist - Associate Director)

Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminal Medicines, Inc.

Boston, MA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 6 hours ago

Job description

About Superluminal Medicines:
Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company's platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.
About the Role:
We are seeking a Machine Learning Scientist to join our integrated discovery team and help advance small molecule drug discovery programs through applied ML. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward program decision points and candidate nomination.
Key Responsibilities:
  • Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
  • Serve as the machine learning POC on cross functional projects partnering with medicinal chemists and structural biologists to refine SAR and structure informed modeling efforts
  • Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
  • May be responsible for management and development of internal team members

Required Qualifications:
  • Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
  • 2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments
  • Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
  • Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones
  • Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows

Preferred Qualifications:
  • Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
  • Expertise fine-tuning existing models with internally generated structural biology and biology data
  • Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
  • Experience mentoring and developing teams

Skills & Competencies:
  • A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
  • Demonstrated expertise using small molecule drug discovery ML/AI tools e.g. AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)
  • Strong level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
  • Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience

Benefits:
Superluminal offers a comprehensive benefits package that fully covers employees' annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.
Equal Opportunity Statement:
Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.

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