AI Research Scientist - Generative Models

Join to apply for the AI Research Scientist - Generative Models role at Radical AI

Join to apply for the AI Research Scientist - Generative Models role at Radical AI

Get AI-powered advice on this job and more exclusive features.

This range is provided by Radical AI. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

Radical AI, Inc. is an artificial intelligence company that is accelerating scientific research & development. We are at the forefront of innovation in the field of materials R&D, a critical driver for advancing our most cutting-edge industries and shaping the future. Breaking away from the traditionally slow and costly R&D process, Radical AI leverages artificial intelligence and machine learning to pioneer generative materials science.

This innovative field blends AI, engineering, and materials science, revolutionizing how materials are created and discovered. Radical AI's approach speeds up R&D and addresses global challenges, setting new benchmarks in technology and sustainability.

The opportunity

As an AI Researcher specializing in generative models for materials discovery at Radical AI, you will help the development of the first commercial foundation model for materials science.

Your role involves pioneering the application of generative models in the field of materials science, in particular by leveraging flow matching, diffusion, and LLMs. You will develop novel models and training techniques tailored for atomistic data in materials science.

Your responsibilities include designing and conducting large-scale experiments to validate the most promising approaches in generative modeling. Collaborating closely with our engineering and computational materials science teams, you will play a crucial role in transitioning state-of-the-art models from research to production. Your expertise will contribute to the broader research community through publications in top-tier AI/ML venues like NeurIPS, ICML, and ICLR, alongside active participation in conferences and workshops.

This role offers the unique opportunity to mentor and guide junior members of our technical team and research interns, fostering a culture of growth and innovation.

Positions are available at various levels of seniority:
Senior, Staff, and Principal.

Mission

• Lead and develop our generative foundation models in materials discovery
• Work in collaboration with various teams, in particular the quantum chemistry team, the atomistic simulation team, the engineering team, and the experimental team
• Conduct large-scale experiments to validate and refine promising approaches, collaborating closely with engineers to implement these models in production
• Develop rigorous benchmarks to validate and test the performance of our models
• Contribute to the AI and materials science research community through publications and active engagement in top-tier AI/ML venues like NeurIPS, ICML, ICLR, and our company blog, alongside active participation in conferences and workshops.
• Tackle challenging problems with new and different ideas, creativity and contrarian thinking
• Mentor and guide junior team members and interns, promoting an environment of continuous learning and innovation

• Ph.D., M.S., or B.S. in Computer Science, AI, Machine Learning, Applied Maths, or a closely related field, with a strong focus on deep learning and generative models
• At least 3 years of research experience in AI, with a proven track record in generative modeling, particularly in fields adjacent to materials science
• Working knowledge of various generative AI architectures, including transformers, diffusion models, CNFs, and flow matching
• Demonstrated experience in designing and running large-scale experiments in AI and machine learning
• Passionate about leveraging ML for science related problems and a commitment to doing world-class research
• Proficiency in Python and relevant ML libraries (e.g., PyTorch)
• Strong publication record in top-tier AI/ML conferences or journals
• Excellent collaboration and communication skills, capable of articulating complex technical ideas clearly and effectively

• Prior…