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Scientific Machine Learning Jobs in Massachusetts

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 · 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 ...

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

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

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

Showing results 21-40

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:
Infographic showing various Scientific Machine Learning job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Principal Machine Learning Scientist

Bayer Inc.

Cambridge, MA

$128K - $192K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 11 days ago


Bayer rating

8.3

Company rating: 8.3 out of 10

Based on 70 frontline employees who took The Breakroom Quiz

25th of 86 rated pharmaceutical


Job description

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility. We're doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining 'impossible'. There are so many reasons to join us. If you're hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there's only one choice.

Principal Machine Learning Scientist 

The Principal Machine Learning Scientist will develop novel machine learning algorithms and workflows for accelerating early-stage drug discovery. In this role, you are responsible for constructing, studying, and training algorithms that learn from complex, high-dimensional data to uncover patterns and develop practical predictive models and applications. Involves utilizing various techniques, such as random forests, deep learning, and neural networks, to enhance the predictive capabilities of algorithms, particularly in natural language processing and machine perception. Focuses on simulating human learning activities, improving system performance through data analysis, and developing deep learning frameworks and systems that operate independently of explicit programming instructions. By continuously refining models and exploring new methodologies, contributes to innovative solutions that leverage machine learning for diverse applications.

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of the Principal Machine Learning Scientist are to:

  • Develop, evaluate, and apply machine learning algorithms and workflows for accelerating early-stage drug discovery, including but not limited to (i) de-novo design of biomolecules, (ii) assessment of target druggability across therapeutic modalities (iii) design of drug delivery systems, (iv) identification of novel druggable pockets and epitopes, (vi) characterization of protein-protein and protein-ligand interactions;
  • Contribute to the implementation, validation, and improvement of machine learning tools and software solutions that support drug discovery activities;
  • Identify opportunities for accelerating ongoing drug discovery projects with internal and external AI capabilities;
  • Communicate, educate, and engage with a broad set of stakeholders (chemists, biologists, computational/data scientists, R&D leadership) on the state of technology and the progress of key internal initiatives. Engage with the broader scientific community through publications, talks, and open-source;
  • Keep up to date with the latest advances in AI-driven modeling of biomolecular structure and dynamics.

 

WHO YOU ARE

Bayer seeks an incumbent who possesses the following:

Required Qualifications:

  • Ph.D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular Engineering, or a related field at the intersection of life sciences and computer science;
  • Deep expertise with state-of-the-art machine learning methods for modeling biomolecules, like co-folding and/or generative methods for protein design;
  • Expertise in handling, processing, integrating and analyzing large datasets related to drug development research, including biochemical, biophysical, and structural biology data;
  • Strong programming skills in Python;
  • Demonstrated commitment to scientific rigor, a track record of scientific excellence, strong analytical thinking, and a high degree of self-motivation;
  • Excellent written and verbal communication.

Preferred Qualifications:

  • 5+ years of relevant post-PhD experience, including 2+ years in industry;
  • Experience with established, physics-based protein modelling methods like Molecular Dynamics and/or Rosetta;
  • Experience in coordinating small, interdisciplinary teams and ability to articulate their impact to managerial stakeholders;
  • Strong record of publications or patents related to machine learning solutions for biomolecular modeling.

Employees can expect to be paid a salary between $128,000.00 - $192,000.00. Additional compensation may include a bonus or commission (if relevant). Additional benefits include healthcare, vision, dental, retirement, PTO, sick leave, etc.

This salary range is merely an estimate and may vary based on an applicant's location, market data/ranges, skills, prior relevant experience, certain degrees and certifications, and other relevant factors.

This posting will be available for application until at least 08/12/2026.

    YOUR APPLICATION      

Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer. 
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below. 

  Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.       Bayer is an E-Verify Employer.               Location: United States : Massachusetts : Cambridge      Division: Pharmaceuticals     Reference Code: 865491          Contact Us     Email: hrop_usa@bayer.com 

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About Bayer

Sourced by ZipRecruiter

Bayer is a global enterprise with core competencies in the life science fields of healthcare and nutrition. We design our products and services to help people and planet thrive by supporting efforts to address the unprecedented global challenges presented by a growing and aging global population. At Bayer, we’re committed to drive sustainable development and generate a positive impact with our businesses. Through bold ideas and unprecedented insights, we’re pioneering new possibilities that advance life for all of us. That means reimagining how we care for ourselves and one another by empowering everyday health, improving approaches to patient care, and finding better ways to nourish our communities around the world.

Industry

Agriculture

Company size

10,000+ Employees

Headquarters location

Whippany, NJ, US