Vice President, Machine Learning

The Company:

FL85 is a Flagship-backed, privately held biotechnology company committed to transforming the current methodology for information molecule therapeutics to tap into their complete therapeutic potential. In recent times, we have started exploiting the power of information molecules to treat historically untreatable diseases and to formulate therapies with extraordinary turnaround times. FL85’s platform integrates nanoparticle development with top-notch informatics technologies and an innovative pipeline of experimentation and discovery to propel a new generation of productive, therapeutically relevant information molecule therapies. We welcome collaborative, relentless problem solvers who are passionate about making a difference to join us!

FL85 was initiated by Flagship Pioneering. Flagship Pioneering concocts, formulates, provides resources, and fosters first-in-category life sciences companies to revolutionize human health and sustainability. Since its inception in 2000, the company has utilized its unique hypothesis-driven innovation process to originate and foster over 100 scientific ventures, resulting in an aggregate value of over $30 billion. The current Flagship ecosystem consists of 37 transformative companies, for instance, Moderna Therapeutics (NASDAQ: MRNA), Indigo Agriculture, Sana Biotechnology (NASDAQ: SANA), Generate Biomedicines, and Tessera Tx.

Position Summary:

FL85 is in search of an experienced and reputable Head of Machine Learning. This individual will steer the vision and implement the strategy and innovation for the machine learning team, in order to enable the company’s novel platform around information molecule delivery.

Responsibilities:

• Chalk out the vision at FL85 for the machine learning team to bring about innovative discoveries in information molecule delivery
  • Take full responsibility for the philosophy and strategic planning behind the core machine learning platform technology and applications
  • Outline and structure a strategy for utilizing generative models on experimental data to model and optimize across a variety of molecule types, from sequence-based designs to diverse, nonlinear macromolecules
  • Quantitatively specify core capabilities of ML platform and its applicability to diverse delivery strategies and its evolution over time
• Design a comprehensive strategy and team for machine learning generation to concurrently support multiple delivery strategies, each with a unique machine learning approach, diverse macromolecule types, and formulations-based data streams
  • Specify team mission, proposed organizational structure, and interaction model to ensure successful implementation of multiple projects simultaneously, while having a leadership role
• Formulate a company-wide roadmap for machine learning to steer decisions across the entire generation engine and formulations pipeline
  • Cooperate closely with scientific leaders of the molecular engine to formulate a roadmap for what kinds of experiments will yield maximum generative machine learning impact
  • Construct a strategy for machine learning applied to biological insights in support of information molecule delivery vehicle generation
• Formulate strategy across multiple streams of in vitro and in vivo data, each with rapidly increasing levels of volume and complexity
  • Collaborate with scientific and informatics teams to formulate strategies for re-normalizing and structuring model data for ideal molecular generation
• Identify and leap upon opportunities for applied research that bolster the platform development and opportunities for therapeutic impact
  • Team up with platform and computational research, knowledge science, and IT colleagues to align data generation with infrastructure, innovative approaches, and data integration strategies
• Design strategy for continuous innovation in machine learning with a 5-year vision
  • This strategy will be outlined both in terms of strategic goals for FL85, data generation requirements to create value, as well as anticipated and actual advances in machine learning
• Embody FL85’s core mission to transform R&D in information molecule medicines as we know it, and foster and maintain a culture at FL85 that is highly resilient, optimistic, innovative, solutions-oriented, transparent, and inclusive

Qualifications:

• A Ph.D. in machine learning, statistics, computer science, mathematical modeling, operations research, or related fields from a recognized higher-education establishment
• Over 10 years of experience in machine learning leadership in the life sciences industry or academia
• Evident mastery of a wide range of machine learning and deep learning methodologies across diverse deep learning architectures, deep learning libraries, and cloud-computing development environments, chiefly focusing on state-of-the-art ML techniques for studying in vivo data as well as generative machine learning techniques across both nucleic acid medicines and various macromolecule types
• Proven ability to innovate on generative machine learning models with fresh architectures, deeply informed views on areas for potential creativity, and a high level of flexibility in structuring models and workflows to adapt to experimental needs and data streams
• Evidence of effortless interaction with, communication to, and motivation of diverse teams, including experimental platform builders. This includes understanding of in vivo screening techniques and how they can integrate into a computational workflow, as well as the ability to clearly and convincingly instruct effective data generation strategies among experimental teams to support model requirements
• Proven ability to seamlessly transition between high level vision and goals and the technical intricacies essential for model development and scaling on a cloud-computing environment. Experience with multi-GPU and multi-node training of industrial scale models will be an advantage
• Proven ability to successfully recruit, hire, and develop a team of machine learning scientists
• Evidence of mentoring and enhancing a team of junior scientists with diverse backgrounds and skillsets, with an emphasis on career growth and advancement of team members as machine learning scientists
• Excellent oral and written communication skills, suitable for both technical and more general audiences
• Proven problem-solving skills, collaborative demeanor and adaptability across disciplines, unquestionable personal integrity, and the ability to attract, inspire, cultivate, and retain a top-tier team at all levels

Location: Cambridge, MA

Flagship Pioneering and our ecosystem companies are committed to equality of employment opportunities