(Sr.) Scientist, GNN's

Company Summary

FL96, Inc. is a privately held, early-stage company developing a novel AI and data-driven approach to materials discovery and development to accelerate the transition to a sustainable economy.

FL96 was founded by Flagship Pioneering, an innovation enterprise that conceives, creates, resources, and builds companies that invent breakthrough technologies to transform health care, agriculture, and sustainability. Flagship has created over 100 groundbreaking companies since 2000, including Moderna (NASDAQ: MRNA), Generate Biomedicines, Tessera Therapeutics, Indigo Ag, Inari Agriculture, and Sana Biotechnology.

Position Summary:

FL96 is seeking a talented (Senior) Machine Learning Scientist. The successful candidate will develop and apply cutting-edge machine learning (ML) approaches to fuel and optimize FL96’s proprietary material synthesis and assay platform, via generative design. This position is ideal for someone with demonstrated depth of expertise in training deep learning models for materials discovery, including functional predictions and deep Density Functional Theory potential approximations.

Key Responsibilities:

• Develop, prototype, and productionize novel ML algorithms for impactful material design
• Leverage open-source deep learning models, data sets, and transfer learning to reduce time to solution
• Develop optimized data pipelines to train deep learning models
• Collaborate closely with experimental material scientists to build data pipelines for ingesting high throughput experimental data and to enable automation
• Communicate findings to a multi-disciplinary audience

Qualifications:

• PhD in machine learning, computer science, or other quantitative disciplines with a strong focus a deep learning applied to materials
• Proven track record of innovation in applied mathematics, data science and ML
• 3+ years hands-on experience in implementing, evaluating and tuning hyperparameters for large deep learning models.
• Strong expertise in multiple programming paradigms and software libraries (e.g. Python, JAX, TensorFlow, PyTorch, Keras, Pandas, sklearn, Hugging Face)
• Experience in materials design is required.
• Strong written and oral communication skills

Preferred Qualifications:

• Candidates with experience training graph neural networks and E3 invariant architectures are encouraged to apply
• Experience with Multi-fidelity machine learning
• Experience leveraging a cloud computing service and more specifically using AWS services such as S3, EC2, and SageMaker
• Experience with Bayesian optimization

About Flagship

Flagship Pioneering is a biotechnology 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.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.