Company SummaryFlagship Labs 97 Inc. (FL97) is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. FL97 is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. Join our mission-driven team and contribute to the future of science.Our Physical Sciences effort is developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.At FL97, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.If this sounds like an environment you'd love to work in, even if you only have some of the experience listed below, please apply.The RoleWe are seeking a Team Lead for Applied Machine Learning (ML) in Physical Sciences to oversee and guide a team of engineers and researchers working on cutting-edge AI models for materials, chemistry, and physics. The Team Lead will drive the development of AI/ML models for materials discovery, foster collaboration across teams, and provide strategic direction for AI integration in the materials science domain.Key Responsibilities:
Lead and mentor a cross-disciplinary team: Supervise and support a group of ML engineers and scientists, guiding them in applying ML techniques to materials composition, structure, and performance.Develop and deploy advanced ML models: Oversee the creation, fine-tuning, and deployment of deep learning models, with a focus on materials discovery, synthesis, and performance prediction.Drive innovation in physics-informed AI: Lead the development of physics-based learning architectures, integrating conservation laws, symmetries, and other scientific principles into AI models.Integrate AI tools with lab workflows: Collaborate closely with experimental teams to design AI-driven methods for lab orchestration, experimental assay design, and optimization of synthesis processes.Oversee computational projects: Ensure team members are successfully implementing deep learning architectures for representation learning, generative AI, and quantitative reasoning tools (e.g., LLMs).Strategize on AI-driven discovery: Shape the team's long-term goals for applying AI to optimize materials discovery, including digital platforms that continually fine-tune models as new data emerges.Communicate findings and strategies: Represent the team's work to stakeholders through presentations, reports, and technical documentation, ensuring clear communication of complex AI-driven insights.Stay at the forefront of AI and materials science: Keep the team up to date with the latest advancements in AI, ML, and materials research, integrating cutting-edge techniques into the team's approach.Must-Have Qualifications:
- 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 with applications in materials composition and performance prediction.
- Strong background in developing physics-informed machine learning models, including conservation laws, symmetry, PINNs, or neural ODEs.
- Proficiency with PyTorch and experience managing multi-GPU training environments.
- Demonstrated track record of publishing scientific papers or contributing to public codebases in the areas of AI and materials science.
- Proficiency in Python and the data science ecosystem (NumPy, SciPy, Pandas), along with data visualization tools.
- PhD in Computer Science, Applied Mathematics, Materials Science, or a related field, with a strong focus on machine learning.
- Excellent communication and leadership skills to manage a diverse team and convey technical findings to stakeholders.
Preferred Qualifications:
- Experience with cloud computing services (e.g., AWS) to optimize training and evaluation processes.
- Familiarity with integrating machine learning into experimental workflows in materials science or chemistry.
- Knowledge of high-throughput experimental platforms for accelerated discovery.
About Flagship:Flagship Pioneering is a platform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity's most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE's Change the World list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company's annual list of the World's Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, FSP ) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.