5+ years research experience in physical sciences or engineering
2-3+ years applying machine learning to technical problems
Strong programming skills in Python and ML frameworks (like PyTorch, TensorFlow)
Deep understanding of scientific computing and numerical methods
Ability to translate scientific challenges into machine learning solutions
Experience in building tools and infrastructure for technical teams
Responsibilities
Integrate machine learning techniques to enhance scientific simulations
Develop AI tools for materials science and device physics
Build AI capabilities to support research teams
Train scientists on incorporating AI/ML into research
Implement physics-informed neural networks for scientific problems
Create data pipelines for scientific machine learning
Collaborate with physicists to identify impactful AI applications
Establish standards for ML-augmented R&D processes
Benefits
Equal employment opportunities for all applicants
Reasonable accommodations provided for applicants with disabilities
Full Job Description
Research Scientist, AI
As a Research Scientist working with AI, you will accelerate and augment R&D workflows by applying machine learning to scientific simulations and modeling while simultaneously building internal AI capabilities across the organization. This role sits at the intersection of cutting-edge physics and artificial intelligence, and you'll work hands-on to develop AI-augmented tools that enable breakthrough research, and build the infrastructure and expertise that empowers our technical teams to leverage AI in their own work. Whether you are a physicist who has embraced machine learning or an AI expert with deep scientific domain knowledge, you will play a pivotal role in defining how we utilize AI to accelerate our own internal R&D.
Responsibilities
Integrate machine learning techniques to accelerate scientific simulations, modeling, and computational workflows
Develop AI-augmented tools for materials science, device physics, or accelerator physics applications
Build internal AI infrastructure and capabilities to enable research teams across the organization
Train and mentor scientists and engineers on integrating AI/ML into their research workflows
Implement surrogate models, physics-informed neural networks, or generative approaches for scientific problems
Develop data pipelines and frameworks for scientific machine learning across distributed teams
Collaborate with computational physicists and experimentalists to identify high-impact AI applications
Set AI best practices and establish standards for ML-augmented R&D
Required Qualifications
5+ years professional or academic research experience in physical sciences, engineering, or related field
2-3+ years hands-on experience applying machine learning to scientific or technical problems
Strong programming skills in Python and ML frameworks (PyTorch, TensorFlow, JAX, or similar)
Deep understanding of scientific computing, numerical methods, and computational modeling
Proven ability to translate scientific problems into machine learning approaches
Experience building tools, infrastructure, or capabilities used by technical teams
Preferred Qualifications
PhD in Physics, Materials Science, Computer Science, Applied Mathematics, or related field
Publications applying ML to scientific computing, simulation, or experimental data analysis
Experience with physics-informed machine learning or scientific foundation models
Background in accelerator physics, semiconductor devices, materials modeling, or related domains
Track record of enabling technical teams through tool development or mentorship
