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Machine Learning For Material Science Jobs (NOW HIRING)

Forhyre

Machine Learning Engineer

New York, NY

We are looking for a Machine Learning Engineer to help us create artificial intelligence products ... If you also have knowledge of data science and software engineering, we'd like to meet you. Your ...

Full Scope

Machine Learning Engineer

Reston, VA

The ideal candidate will have a strong background in machine learning, data science, and software ... Optimize and fine-tune models for performance, accuracy, and scalability. * Deploy machine learning ...

Aivra Health LLC

$28 - $45/hr

This role is ideal for students or entry level candidates in STEM fields who are passionate about ... The intern will work closely with Data Scientists and Software Engineers to develop, train ...

Shaped

Senior Machine Learning Engineer

Brooklyn, NY · On-site +1

$130K - $200K/yr

Company Description Shaped is an API for developers to seamlessly add personalized ranking and ... Bachelor's in computer science, data science or mathematics related field. Master's degree or PhD ...

Shaped

Senior Machine Learning Engineer

Brooklyn, NY · On-site

$130K - $200K/yr

Company Description Shaped is an API for developers to seamlessly add personalized ranking and ... Bachelor's in computer science, data science or mathematics related field. Master's degree or PhD ...

Aivra Health LLC

$28 - $45/hr

This role is ideal for students or entry level candidates in STEM fields who are passionate about ... The intern will work closely with Data Scientists and Software Engineers to develop, train ...

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All JobsMachine Learning For Material Science Jobs

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 8, 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

Machine Learning Engineer

Dark Wolf Solutions

Herndon, VA • On-site

Full-time

Posted 23 days ago

Job description

Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges.
Dark Wolf is seeking a highly motivated and self-directed professional to fill the role of Machine Learning (ML) Engineer to support our team in Northern Virginia.
Responsibilities:
  • Design, develop, and implement machine learning models and algorithms to solve specific business problems.
  • Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
  • Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure.
  • Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions.
  • Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems.
  • Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability.
  • Monitor and maintain deployed models, ensuring their reliability and performance in production environments.
  • Troubleshoot and resolve issues related to machine learning models and pipelines.
  • Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields.
  • Contribute to the development of best practices and standards for machine learning development and deployment within the team.
  • Document machine learning models, experiments, and deployment processes.
  • Potentially work with large datasets and big data technologies.
  • Optimize machine learning models for performance and efficiency.

Qualifications:
  • Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields.
  • Demonstrated hands-on experience in developing and deploying machine learning models in a production environment.
  • Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc.
  • Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures).
  • Experience with data preprocessing, feature engineering, and data visualization techniques.
  • Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop).
  • Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services.
  • Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines.
  • Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions.
  • Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences.

Preferred Skills:
  • Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems.
  • Experience with MLOps practices and tools for automating and monitoring machine learning workflows.
  • Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
  • Experience with building and deploying RESTful APIs.
  • Familiarity with big data technologies and distributed computing.
  • Experience with statistical modeling and inference.

Position Clearance Requirement:
TS/SCI with Full-Scope Polygraph
This position is located in Chantilly/Herndon, VA.
We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

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