Machine Learning Scientist II/Senior Scientist, Chemistry at Flagship Pioneering, Inc.

Cambridge, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

21 Apr, 26

Salary

231000.0

Posted On

21 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Computational Chemistry, Python, Deep Learning, Data Analysis, Model Validation, Cloud Computing, Containerization, Experimental Feedback, Generative Modeling, Thermodynamics, Statistical Mechanics, Bayesian Optimization, Active Learning, Reinforcement Learning, DFT

Industry

Biotechnology Research

Description

COMPANY DESCRIPTION Our start-up is building a novel chemical discovery platform to reshape how we develop formulations at the molecular level. By combining artificial intelligence with a cutting-edge laboratory discovery pipeline, our interdisciplinary team will chart an unexplored chemical landscape, enabling breakthroughs in drug delivery, agrichemical formulation, and safe, high-performance industrial chemicals. Our company is backed by Flagship Pioneering, a biotechnology origination company that has founded and developed over 115 scientific ventures over the past 25 years. Flagship Pioneering is dedicated to the development of an ecosystem of first-in-category life sciences companies, resulting in $20+ billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents. THE ROLE We are seeking a motivated Senior ML Scientist to lead the predictive pipeline of a lab-in-the-loop platform for novel chemistry. You will define and implement models that reason through the physical rules that govern chemistry to predict the properties of physiochemical interactions at the molecular level. The ideal candidate is eager to take the lead on building a chemistry discovery platform from scratch and be the team’s computational visionary. KEY RESPONSIBILITIES Lead the design, development and validation of models that predict phase behaviors and physiochemical properties of chemical mixtures across temperature, hydration, and concentration gradients Architect and own a generative “lab-in-the-loop” modeling pipeline, that incorporates human input of the chemical formulation scientist, integrates experimental feedback and proposes the next set of experimental samples to be tested in the wet lab Build on top of next-generation chemistry ML methods, such as chemical LLMs, reaction prediction, and electronic structure methods like DFT Own the data flow pipeline, including definition of data standards and processing, data distribution analysis, and visualization to guide the team and inform decision-making processes Act as a technical thought partner to chemists, experimental scientists, and leadership, translating scientific questions into modeling strategy and experimental design PROFESSIONAL EXPERIENCE & QUALIFICATIONS PhD (or equivalent experience) in machine learning, computational chemistry, organic chemistry, chemical engineering, materials science, or related field Demonstrated experience leading or independently driving the development, training, and validation of ML models in chemistry, materials science, or related domains Knowledge of fundamentals of chemical properties is essential Proficiency in Python-based deep-learning frameworks (e.g. torch, PyTorch lightning), ML logging software (e.g. MLFLow, WandB), relevant scientific libraries for chemistry (e.g. RDKit), and hyper-parameter optimization (e.g. Optuna) Ability to design, maintain, and evolve Python compute environments and ML infrastructure Strong code organization and reproducibility work ethics and usage of git-based workflows Familiarity with cloud/HPC frameworks (e.g. AWS, azure), containerization (e.g. docker), and batch compute Experience handling large, noisy experimental datasets Strong team player: ability to communicate and work hand-in-hand with chemists, lab scientists, and operational team members Experience with DFT and molecular dynamics tools preferred Experience in Bayesian optimization, active learning, reinforcement learning preferred Prior work on lab-in-the-loop platforms or early-stage teams preferred Knowledgeable in thermodynamics, statistical mechanics, and hydrogen-bonding interactions; familiarity with phase-diagram analysis preferred LOCATION: Cambridge, MA ABOUT FLAGSHIP PIONEERING: Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies 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. At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact. We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship. 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. #LI-TL1 The salary range for this role is $152,000 - $231,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. FL118 currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on FL118's good faith estimate as of the date of publication and may be modified in the future.

Responsibilities

Lead the design and development of models predicting phase behaviors and physiochemical properties of chemical mixtures. Architect a generative modeling pipeline that integrates human input and experimental feedback.