Head of Machine Learning

The Company:

FL85 is a Flagship backed, privately held biotechnology company on a mission to transform the current approach to information molecule therapeutics to unlock their full therapeutic potential. In recent years, we have begun to experience the power of information molecules in treating historically undruggable diseases and in designing therapies with unprecedented turnaround times. FL85’s platform integrates nanoparticle development with world-class informatics technologies and a novel pipeline of experimentation and discovery to drive a new generation of highly effective, therapeutically relevant information molecule therapies. We are seeking collaborative, relentless problem solvers that share our passion for impact to join us!

FL85 was founded by Flagship Pioneering. Flagship Pioneering conceives, creates, resources, and develops first-in-category life sciences companies to transform human health and sustainability. Since its launch in 2000, the firm has applied a unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $30 billion in aggregate value. The current Flagship ecosystem comprises 37 transformative companies, including: Moderna Therapeutics (NASDAQ: MRNA), Indigo Agriculture, Sana Biotechnology (NASDAQ: SANA), Generate Biomedicines, and Tessera Tx. 

Position Summary:

FL85 is seeking an experienced and respected Head of Machine Learning. This person will drive the vision and execution of strategy and innovation for the machine learning team, to enable the company’s novel platform around information molecule delivery.

Responsibilities:

• Develop the vision at FL85 for the machine learning team to drive novel discoveries in information molecule delivery
• Own philosophy and strategy behind core machine learning platform technology and applications
• Define and build a strategy for using generative models on experimental data to model and optimize across a variety of types of molecules, from sequence-based designs to diverse, nonlinear macromolecules
• Quantitatively define core capabilities of ML platform and application to diverse delivery strategies and how this will improve and change over time
• Build a comprehensive strategy and team for machine learning generation to support multiple delivery strategies simultaneously, each with a unique machine learning approach, diverse macromolecule types, and formulations-based data streams
• Define team mission, proposed org structure, and interaction model to ensure successful execution of multiple projects in parallel, playing a leadership role
• Create a company-wide roadmap for machine learning to drive decisions across the entire generation engine and formulations pipeline
• Work closely with scientific leaders of the molecular engine to develop a roadmap for what types of experiments will drive maximal generative machine learning impact
• Create a strategy for machine learning applied to biological insights in support of information molecule delivery vehicle generation
• Build strategy across multiple streams of in vitro and in vivo data, each with fast-scaling levels of volume and complexity
• Work with scientific and informatics teams to develop strategy for re-normalizing and structuring model data for ideal molecular generation
• Identify and act upon opportunities for applied research that further the platform buildout and opportunity for therapeutic impact
• Partner with platform and computational research, knowledge science and IT colleagues to align data generation with infrastructure, innovative approaches, and data integration strategies
• Develop strategy for continual innovation in machine learning with 5-year vision
• This will be defined across both strategic goals for FL85, data generation requirements to create value as well as anticipated and realized advances in machine learning
• Embody FL85’s core mission to transform R&D in information molecule medicines as we know it, and live and set a culture at FL85 that is highly resilient, optimistic, innovative, solutions-oriented, transparent, and inclusive

Qualifications:

• Ph.D. in machine learning, statistics, computer science, mathematical modeling, operations research, or related fields from a recognized higher-education establishment
• >10 years of experience of machine learning leadership in the life sciences industry or in academia
• Demonstrated mastery of a broad array machine learning and deep learning approaches across a variety of deep learning architectures, deep learning libraries, and cloud-computing development environments, with an emphasis on cutting edge ML techniq