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Machine Learning For Material Science Jobs (NOW HIRING)

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

... tests for ML workflows Quali fications MS in Computer Science, Data Science, Statistics ... Experience in an industry setting related to biotechnology, chemicals, or materials manufacturing

Nanite Inc.

Machine Learning Engineer

Boston, MA

... tests for ML workflows Quali fications MS in Computer Science, Data Science, Statistics ... Experience in an industry setting related to biotechnology, chemicals, or materials manufacturing

Amat

Physicist/Scientist Machine Learning

Santa Clara, CA · On-site +1

$138K - $190K/yr

If you want to push the boundaries of materials science and engineering to create next generation ... This role is ideal for candidates with strong domain knowledge in engineering or physical sciences ...

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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.

Machine Learning Engineer

Nanite Inc.

Boston, MA • On-site

Full-time

Posted 29 days ago

Job description

Our mission is to deliver the undeliverable.
Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug delivery. The research intern will be in a fast-paced start-up environment playing a crucial technical role in generating cell culture and transfection data. The candidate will work with senior leadership and partner projects gaining broad internal and external exposure.
Essential Functions and Duties
  • Design and implement complex data engineering processes to support innovative data science modeling
  • Collaborate with chemistry and biology research teams to design data pipelines, analyze experimental data and implement experimentally actionable feed-back loops
  • Apply and deploy established and novel statistical and machine learning algorithms to explore, understand and optimize properties of the vast delivery vehicle space, both in silico and experimentally
  • Develop robust, scalable workflows and maintain security controls to protect sensitive data across cloud and on-premise environments
  • Coordinate with cross-functional teams to deploy models and communicate results and with a focus on computational efficiency, performance, and usability
  • Design of repositories, CI/CD pipelines and integration tests for ML workflows

Qualifications
MS in Computer Science, Data Science, Statistics, Computational Biology, Computational Chemistry, or a related discipline with 2 years hands-on machine learning experience.
Knowledge, Skills, and Abilities
  • Track record developing statistical and machine learning models for complex and unconventional real-life problems
  • Strong mathematical and coding skills
  • Proficiency in Python, MLOps (W&B, MLFlow) and ML packages (scikit-learn, PyTorch, JAX), along with SQL and AWS.
  • Familiarity with ML workflow best practices.
  • Interest in applications of machine learning in biotechnology
  • Strong communication skills, both written and verbal
  • Experience doing research and working with interdisciplinary teams

Additional Preferred Experience (desired, but not essential):
  • Experience in an industry setting related to biotechnology, chemicals, or materials manufacturing
  • Experience with cheminformatics, computational chemistry, computational biology databases, data structures, material science and modelling package

Computer and modeling work required, this is an on-site position based in the Seaport of Boston, MA.

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