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.

Responsibilities

• Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance
• Implement best practices and innovate methods for uncertainty quantification
• Combine datasets of multiple fidelities and sources to power data-driven materials discovery
• Work with the computational team to identify materials design pathways that target desired functional properties and their synthesis
• Work with infrastructure and automation teams to transfer data and predictions in real time
• Work with the experimental team to drive material discovery and development, and build domain-specific acquisition functions.
• Continually cultivate scientific/technical expertise through critical review of ML literature, attending conferences, and developing relationships with key opinion leaders
• Report findings to stakeholders and leadership in written reports and verbal presentations.

Qualifications

• Experience with uncertainty quantification, active learning and Bayesian Optimization
• Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks) on small and large datasets.
• Strong experience in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in Python data science ecosystem (Numpy, SciPy, Pandas, etc.)
• Experience using a cloud computing service to reduce runtime to train and evaluate deep learning models
• PhD in Computer Science, Applied Mathematics, quantitative disciplines with strong focus in ML, or related field
• Strong self-starter and independent thinker, with strong attention to detail
• Demonstrated industry experience or academic achievement
• Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
• Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting

Preferred Qualifications

• Experience using AWS services
• Experience with machine learning integration in experiment workflows

 

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.