Research Scientist, AIAs 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
Salary Range$150,000-$275,000 USD
