Materials AI/ML Specialist

3M•Maplewood, MN

3d•Onsite

About The Position

Collaborate with Innovative 3Mers Around the World. This position provides an opportunity to transition from other private, public, government or military experience to a 3M career. The Impact You’ll Make in this Role As a Materials AI/ML Specialist, you will apply artificial intelligence and machine learning techniques to accelerate new product development in formulation science. Partnering closely with product developers, you will help modernize how experiments are planned, run, and analyzed - bringing data-driven speed to lab-based innovation. Here, you will make an impact by: Applying AI/ML methods (e.g., Bayesian optimization, cheminformatics, active learning, AI hypothesis generation ) to guide and accelerate experimental planning for new liquid adhesive and pressure-sensitive adhesive (PSA)/tape formulations Running hands-on formulation, mechanical, and performance testing (e.g., overlap shear, DMA, handling strength) to generate high-quality data for AI/ML-driven analysis Analyzing formulation, process, and performance data using statistical and machine learning approaches to extract insights that drive faster, better technical decisions Continuously expanding your AI/ML capabilities - including techniques such as Bayesian optimization and generative AI/LLM-assisted literature mining - and applying them to live product development challenges Using and helping evolve shared digital tools and platforms for experiment planning, data analysis, and knowledge capture Sharing insights and learnings across programs to strengthen data-driven development practices more broadly Communicating technical findings clearly to cross-functional stakeholders, including chemists, engineers, and business leaders Balancing lab time and analytical work across multiple concurrent development programs Operating with a high degree of independence in a fast-paced R&D environment, quickly picking up new formulation, testing, and lab safety protocols as needed

Requirements

Bachelor's degree in Chemistry, Materials Science, Chemical Engineering, or a related field (completed and verified prior to start)
Three (3) years of experience in new product development, formulation, or experimental/lab work, in an academic, private, public, government, or military environment
Two (2) experience using AI/ML or data science tools within your own experimental/lab work (e.g., machine learning, AI hypothesis generation, design of experiments software, statistical modeling, data analysis tools) , with a demonstrated desire and aptitude to expand this skill set

Nice To Haves

Master's degree or higher in Chemistry, Materials Science, Chemical Engineering, or a related field
Five (5) or more years of hands-on new product development or formulation experience
Hands-on experience with adhesive, PSA, tape, coating, or polymer formulation and mechanical testing (e.g., overlap shear, DMA, handling strength)
Familiarity with Bayesian optimization, active learning, surrogate modeling, or other sequential/statistical design-of-experiments techniques
Exposure to large language models (LLMs) or generative AI tools for materials discovery, hypothesis generation, or literature mining
Working knowledge of Python and common ML/data science libraries (e.g., PyTorch, Scikit-Learn, Pandas)
Strong communication and collaboration skills, with the ability to work effectively across multiple teams and stakeholder groups
Comfortable managing competing priorities across multiple concurrent programs in a fast-paced, evolving R&D environment
Curious, fast learner, solid communicator, self-directed in acquiring new technical skills

Responsibilities

Applying AI/ML methods (e.g., Bayesian optimization, cheminformatics, active learning, AI hypothesis generation ) to guide and accelerate experimental planning for new liquid adhesive and pressure-sensitive adhesive (PSA)/tape formulations
Running hands-on formulation, mechanical, and performance testing (e.g., overlap shear, DMA, handling strength) to generate high-quality data for AI/ML-driven analysis
Analyzing formulation, process, and performance data using statistical and machine learning approaches to extract insights that drive faster, better technical decisions
Continuously expanding your AI/ML capabilities - including techniques such as Bayesian optimization and generative AI/LLM-assisted literature mining - and applying them to live product development challenges
Using and helping evolve shared digital tools and platforms for experiment planning, data analysis, and knowledge capture
Sharing insights and learnings across programs to strengthen data-driven development practices more broadly
Communicating technical findings clearly to cross-functional stakeholders, including chemists, engineers, and business leaders
Balancing lab time and analytical work across multiple concurrent development programs
Operating with a high degree of independence in a fast-paced R&D environment, quickly picking up new formulation, testing, and lab safety protocols as needed