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Machine Learning Material Science Jobs (NOW HIRING)

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... material science and modelling package Computer and modeling work required, this is an on-site ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... material science and modelling package Computer and modeling work required, this is an on-site ...

Tranzeal

Materials Science Ai Engineer

Santa Clara, CA · Hybrid

Santa Clara, CA Number of days onsite - 5 Days Need candidates with a PhD (Material Science or Machine learning preferred) Must Have Skills Skill 1 - Strong proficiency in programming languages like ...

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 ... Significant experience developing machine learning or deep learning models using data from ...

LLNL

Machine Learning Research Engineer

Livermore, CA · On-site

$146.34K - $222.56K/yr

We have an opening for Machine Learning Research experts to join our team and advance the ... physics, material science, predictive medicine, and treatment discovery. You will also have the ...

New

LLNL

Machine Learning Research Engineer

Livermore, CA

$146.34K - $222.56K/yr

We have an opening for Machine Learning Research experts to join our team and advance the ... physics, material science, predictive medicine, and treatment discovery. You will also have the ...

New

CloudDevs

Machine Learning Intern

Manhattan, NY · Remote

Requirements Pursuing a degree in Computer Science, Data Science, or related field. Strong understanding of machine learning concepts and algorithms. Proficiency in programming languages such as ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ... Experience working with scientific or time-series datasets, especially in battery, materials, or ...

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Machine Learning Material Science information

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

To thrive as a Machine Learning Material Scientist, you need a strong background in materials science, mathematics, and machine learning, often supported by an advanced degree in a related field. Proficiency with programming languages like Python, data analysis tools, and machine learning frameworks such as TensorFlow or scikit-learn is typically required. Strong problem-solving abilities, effective communication, and collaborative skills are essential for interdisciplinary teamwork and conveying complex findings. These skills are crucial for developing innovative materials solutions and accelerating research and development through data-driven approaches.

How do professionals in machine learning material science typically collaborate with experimental scientists and engineers?

In machine learning material science, collaboration with experimental scientists and engineers is essential. Professionals in this role often work closely with lab teams to understand experimental data, design new experiments, and validate machine learning predictions. Regular meetings, shared databases, and interdisciplinary project teams are common, allowing for feedback and integration of computational results with physical testing. This collaborative environment ensures that machine learning models are grounded in real-world data and that discoveries can be rapidly prototyped and tested.

What is a Machine Learning Material Scientist?

A Machine Learning Material Scientist is a professional who combines expertise in materials science with advanced knowledge of machine learning techniques. They use algorithms and data-driven models to analyze, predict, and design materials with desirable properties for various applications. This interdisciplinary role often involves processing large datasets, developing predictive models, and collaborating with experimentalists to accelerate materials discovery and optimization.

What is the difference between Machine Learning Material Science vs Materials Engineer?

AspectMachine Learning Material ScienceMaterials Engineer
Required CredentialsMaster's or PhD in Data Science, Materials Science, or related fieldsBachelor's or Master's in Materials Engineering or related disciplines
Work EnvironmentResearch labs, tech companies, academia focusing on data-driven modelingManufacturing plants, R&D labs, construction sites
Industry UsageDeveloping algorithms to predict material properties, optimizing material designDesigning, testing, and implementing new materials for products and structures

Machine Learning Material Science focuses on applying data science and machine learning techniques to understand and predict material behaviors, while Materials Engineers work on designing, testing, and developing new materials through traditional engineering methods. Both roles are essential in advancing material innovation but differ in their approach and skill sets.

Infographic showing various Machine Learning Material Science job openings in the United States as of May 2026, with employment types broken down into 87% Full Time, 8% Part Time, 3% Temporary, 1% Contract, and 1% Nights. Highlights an 75% Physical, 1% Hybrid, and 24% Remote job distribution.

Machine Learning Engineer

Nanite Inc.

Boston, MA • On-site

Full-time

Posted 25 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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