Senior Applied AI/ML Scientist

Key Responsibilities

• Design, develop, and deploy machine learning and deep learning models for scientific applications (e.g., materials discovery, chemical modeling, process optimization)
• Translate complex scientific and business problems into tractable AI/ML frameworks
• Work with real-world structured and unstructured scientific data (e.g., experimental, simulation, and literature data)
• Build and maintain data pipelines, feature engineering workflows, and model evaluation frameworks
• Collaborate cross-functionally with scientists, engineers, and product managers to deliver production-ready solutions
• Partner closely with Customer Success and client-facing teams to understand customer needs, translate requirements into AI/ML solutions, and support the successful deployment, adoption, and ongoing optimization of models in customer environments
• Apply techniques such as supervised/unsupervised learning, generative models, and optimization algorithms
• Contribute to the integration of models into scalable software platforms and APIs
• Stay current with advancements in AI/ML and relevant scientific domains; evaluate and apply new methods where appropriate
• Communicate findings and model outputs clearly to both technical and non-technical stakeholders
• Periodic travel to customer sites and attend industry events

Requirements

• Ph.D. in Chemistry, Physics, Biochemistry, Chemical Engineering, or other STEM-related fields
• Degree or coursework in Machine Learning
• Hands-on experience applying machine learning to real-world problems in science and engineering
• Strong background in machine learning: classical and deep learning techniques (examples may include GNNs, transformers, or embedding techniques, etc.)
• Strong experience in Python and associated ML frameworks (Pytorch, Tensorflow, Keras, sklearn, etc.)
• Demonstrated ability to effectively communicate complex technical details at a high level
• Solid understanding of statistical modeling, optimization, and algorithm design
• Proven ability to deploy models into production environments

Preferred

• Experience in either representation learning, geometric learning, uncertainty quantification, or unsupervised learning approaches
• Experience with cloud or distributed training frameworks (Azure, AWS, GCP) and MLOps practices
• Experience in scientific domains such as chemistry, materials science, or physics
• Strong familiarity with molecule generation, chemical foundation models, and/or physics-informed ML
• Experience with generative AI methods (e.g., diffusion models, flow matching, transformers) applied to scientific problems

Benefits

• Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance
• Flexible paid time off & generous holidays
• Remote-first with co-working access at Industrious offices
• 401(k) with employer match
• Equity package
• Base Salary Range $190k - $220k (Depending on experience & Geographic location)
• Performance-based bonus plan