As the largest pureplay adhesives company in the world, H.B. Fuller’s (NYSE: FUL) innovative, functional coatings, adhesives and sealants enhance the quality, safety and performance of products people use every day. Founded in 1887, with 2025 revenue of $3.5 billion, our mission to Connect What Matters is brought to life by more than 7,100 global team members who collaborate with customers across more than 30 market segments in 150 countries to develop highly specified solutions that enable customers to bring world-changing innovations to their end markets. Learn more at www.hbfuller.com.
What you can expect from H.B. Fuller’s Internship Program:
You’ll be joining a cohort of talented students from a variety of schools across the U.S. We hire interns in the following positions: Process Engineering, R&D, Strategic Sourcing, Technical Sales, Supply Chain, Product Marketing, Information Technology, and more.
The projects we task our interns to lead are impactful and meaningful to our organization. You’ll be supported by your Manager and Mentor to gain in-depth knowledge of the adhesive industry and collaborate with various departments and functions. We also offer an established path for next-level opportunities – making this internship the starting point for a long-term career path.
Location: Saint Paul, Minnesota
Department: Research & Development
Our Internship Experience includes:
12-week paid program beginning the end of May to mid-August to gain insight and knowledge in your field.
Interactive orientation and events with skilled professionals in your field, including managers, directors, and CEO.
Multiple networking, volunteer and fun events – both virtual and in-person.
Impactful projects that make a difference internally and externally for H.B. Fuller.
End of summer final presentation to showcase your career development with support from managers and peers.
Position Overview:
The R&D (Materials Data) Intern will work at the intersection of machine learning, chemistry, and polymer science to accelerate the development of next-generation products within the adhesives market. This role will focus on developing AI and machine learning workflows that enable scientists to make data-driven formulation and materials design decisions.
The ideal candidate possesses a strong foundation in chemistry, polymer science, or materials science and has demonstrated experience applying machine learning and data science techniques to scientific problems. This position is particularly well suited for students interested in careers at the intersection of computational science, AI, and materials innovation.
Primary Responsibilities:
Collaborate with R&D scientists to collect, curate, and structure experimental data for machine learning applications.
Develop and validate interpretable machine learning models to predict critical material and formulation properties.
Analyze model outputs to identify meaningful structure-property relationships and generate scientific insights.
Apply machine learning techniques to propose novel formulations and candidate materials meeting targeted performance requirements.
Develop workflows for data preprocessing, feature engineering, model training, and model evaluation.
Build automated data pipelines that integrate historical data sources and laboratory-generated datasets.
Communicate technical findings and recommendations to multidisciplinary teams of scientists and engineers.
Minimum Qualifications:
Currently pursuing a Bachelor's degree in Materials Science & Engineering, Chemistry, Chemical Engineering, Polymer Science, or a related scientific discipline.
Demonstrated proficiency in Python and common scientific computing libraries, including Pandas, NumPy, Scikit-learn, and PyTorch or TensorFlow.
Strong understanding of machine learning fundamentals, including model development, validation, and interpretation.
Strong theoretical understanding of chemistry, polymer science, or materials science principles.
Excellent written and verbal communication skills and the ability to effectively collaborate with multidisciplinary scientific teams.
Preferred Qualifications
Double major, minor, or significant coursework in Computer Science, Data Science, Statistics, or a related field.
Research experience involving machine learning, computational chemistry, materials informatics, polymer science, or related disciplines.
Experience working with molecular, materials, chemical, or formulation datasets.
Familiarity with feature engineering, dimensionality reduction, optimization methods, or generative AI approaches for scientific applications.
Pay is based on several factors including but not limited to education, work experience, certifications, and geographic location.
The salary for this role is $22-$25 per hour.In addition to your salary, H.B. Fuller offers employees a competitive total rewards package including comprehensive benefits, incentive and recognitions programs, health & wellness benefits, 401K contributions, paid time off and paid holidays. Eligibility may vary.
H.B. Fuller is an Equal Employment Opportunity employer and proud to have created a collaborative culture where employees around the world are seen, heard, and respected. All qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, national origin, disability, or marital status or status as a protected veteran, or any other legally protected classification.
H.B. Fuller does not accept unsolicited resumes from recruiters, employment agencies, or staffing firms. To conduct business with H.B. Fuller, a written service agreement must be executed by Human Resources prior to submitting any information relating to a potential candidate. Without a signed service agreement, H.B. Fuller shall not be obligated for payment of any fee or compensation.
Job details are sourced from the employer's original posting.
Open job postingAbout the company
H.B. Fuller is a global company that develops and manufactures adhesives, sealants, and coatings. Their products enhance the quality, safety, and performance of everyday items across various market segments.