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Machine Learning For Material Science Jobs (NOW HIRING)

... 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 ...

... systems for quantum processors. The role involves developing ML solutions that enhance the ... training materials. Qualifications : Required : • PhD/Master in Machine Learning, Physics ...

$123K - $185K/yr

... 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 ...

Irving, TX Responsibilities Predictive Modeling for Material Property Design * Develop and apply ... Proficiency in AI + physics-based machine learning. * Working understanding of material science ...

... battery products for next-generation energy storage system (ESS) and electric vehicle (EV ... Experience working with scientific or time-series datasets, especially in battery, materials, or ...

... battery products for next-generation energy storage system (ESS) and electric vehicle (EV ... Experience working with scientific or time-series datasets, especially in battery, materials, or ...

Stay updated with the latest trends and technologies in data science and machine learning. Basic Qualifications: Proficient in Python, Pandas, NumPy, Scikit-Learn, PySpark Bachelor s degree in ...

... models for performance and efficiency. Qualifications : Required : • Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry ...

Machine Learning

Mountain View, CA · On-site

$220K - $331K/yr

... for AI-powered products). * OR Bachelor's Degree in Computer Science, Mathematics, Machine Learning, Physics, or related field AND 8+ years data-science experience (e.g., managing structured and ...

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Machine Learning For Material Science information

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$24.5K

$48.4K

$79K

How much do machine learning for material science jobs pay per year?

As of Jun 7, 2026, the average yearly pay for machine learning for material science in the United States is $48,391.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,500.00 and $52,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Machine Learning for Material Science professional, you need a solid background in materials science, statistics, and programming, typically supported by an advanced degree in a related field. Experience with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), data analysis tools, and familiarity with high-performance computing are vital. Strong problem-solving skills, interdisciplinary communication, and curiosity stand out as essential soft skills for this role. These skills and qualities are crucial for developing innovative solutions, interpreting complex data, and effectively collaborating with both computational and experimental teams.

What is the difference between Machine Learning For Material Science vs Data Scientist?

AspectMachine Learning For Material ScienceData Scientist
Required CredentialsDegree in Materials Science, Computer Science, or related fields; knowledge of machine learning and materials dataDegree in Statistics, Computer Science, or related fields; proficiency in programming and data analysis
Work EnvironmentResearch labs, R&D departments, academia, industry focused on materials developmentBusiness, tech companies, finance, healthcare, with focus on data analysis and insights
Employer & Industry UsageMaterials manufacturing, aerospace, automotive, academiaTech firms, consulting, finance, healthcare, various industries

While both roles involve data analysis and machine learning, Machine Learning For Material Science specializes in applying these techniques to materials data and development, whereas Data Scientists work across diverse industries analyzing broad datasets to generate insights and support decision-making.

What are some common challenges faced by machine learning professionals working in material science, and how can they be addressed?

One of the main challenges in applying machine learning to material science is the limited availability and quality of experimental data, which can make it difficult to train robust models. Additionally, integrating domain knowledge from material science with machine learning techniques requires close collaboration with subject matter experts. Professionals often address these challenges by using data augmentation, transfer learning, and active learning strategies, as well as working in interdisciplinary teams to ensure that models are both accurate and scientifically meaningful.

What is machine learning for material science?

Machine learning for material science refers to the application of machine learning algorithms and data-driven techniques to solve problems in materials discovery, design, and analysis. By leveraging large datasets and computational models, researchers can predict material properties, optimize processes, and accelerate the development of new materials. This interdisciplinary approach combines expertise from computer science, materials engineering, and physics to make materials research more efficient and innovative.
Infographic showing various Machine Learning For Material Science job openings in the United States as of May 2026, with employment types broken down into 3% Locum Tenens, 17% Full Time, 77% Part Time, and 3% Temporary. Highlights an 75% Physical, 1% Hybrid, and 24% Remote job distribution, with an average salary of $48,391 per year, or $23.3 per hour.
Principal Machine Learning Scientist

Principal Machine Learning Scientist

Bayer

Cambridge, MA

$123K - $185K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 2 days ago


Bayer rating

8.1

Company rating: 8.1 out of 10

Based on 65 frontline employees who took The Breakroom Quiz

31st of 71 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 $123,760.00 - $185,640.00

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 04/17/2026. 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