Lead, Applied Machine Learning (Materials Science)

Job Application For Research Associate / Senior Research Associate, Vaccine Researchflagship Pioneering, Inc. - Cambridge, MA

Full-time - Senior

Cambridge, MA

51-100 employees

About the position

The Team Lead for Applied Machine Learning (ML) in Physical Sciences at Flagship Labs 97 Inc. (FL97) is responsible for overseeing a team of engineers and researchers focused on developing advanced AI models for materials discovery. This role involves guiding the application of machine learning techniques to enhance materials composition, structure, and performance, while fostering collaboration across teams and providing strategic direction for AI integration in the materials science domain.

Responsibilities

• Lead and mentor a cross-disciplinary team of ML engineers and scientists.
• Develop and deploy advanced ML models for materials discovery and performance prediction.
• Drive innovation in physics-informed AI by integrating scientific principles into AI models.
• Integrate AI tools with lab workflows to optimize experimental processes.
• Oversee computational projects involving deep learning architectures and generative AI.
• Strategize on AI-driven discovery to optimize materials discovery processes.
• Communicate findings and strategies to stakeholders through presentations and reports.
• Stay updated with advancements in AI, ML, and materials research.

Requirements

• Proven experience in leading teams in AI/ML applied to physical sciences, particularly in materials science, chemistry, or physics.
• Expertise in training, deploying, and fine-tuning deep learning models for materials applications.
• Strong background in developing physics-informed machine learning models.
• Proficiency with PyTorch and experience managing multi-GPU training environments.
• Demonstrated track record of publishing scientific papers or contributing to public codebases in AI and materials science.
• Proficiency in Python and the data science ecosystem (NumPy, SciPy, Pandas).
• PhD in Computer Science, Applied Mathematics, Materials Science, or a related field with a focus on machine learning.
• Excellent communication and leadership skills.

Nice-to-haves

• Experience with cloud computing services (e.g., AWS) for optimizing training processes.
• Familiarity with integrating machine learning into experimental workflows in materials science or chemistry.
• Knowledge of high-throughput experimental platforms for accelerated discovery.

Benefits

• Competitive salary and equity options.
• Health, dental, and vision insurance.
• Flexible work hours and remote work options.
• Professional development opportunities.
• Collaborative and inclusive work environment.