Senior Machine Learning Scientist at Flagship Pioneering, Inc.

Cambridge, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Dec, 25

Salary

0.0

Posted On

27 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Active Learning, Bayesian Optimization, Drug Delivery, Chemoinformatics, Python, Cloud Computing, Data Science, Uncertainty Quantification, Lipid Nanoparticle, Collaboration, Problem Solving, Communication, Presentation Skills, Cross-Functional Teams, Experimental Design

Industry

Biotechnology Research

Description

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), Rubius Therapeutics (NASDAQ: RUBY), Indigo Agriculture, and Sana Biotechnology (NASDAQ: SANA). The Position Mirai Bio is seeking a highly talented individual to develop novel ML approaches to optimize in vivo therapeutic delivery vehicles. They will work cross-functionally to identify and test therapeutic candidates to enable Mirai’s next generation genomic medicines. The successful candidate will implement strategies to overcome limitations of current nucleic acid delivery approaches and will have a strong understanding of targeted information molecule delivery. A successful candidate will have strong familiarity with active learning, multi-objective optimization and experience with chemoinformatic libraries. The candidate will also be expected to collaborate extensively with experimental teams. Responsibilities Design, build, and scale supervised ML models for active learning and Bayesian Optimization of lipid nanoparticle (LNP) formulation, synthesis, and in vivo performance Develop and implement best practices for uncertainty quantification in LNP potency and biodistribution Integrate multi-fidelity datasets (in silico and in vivo) to accelerate data-driven discovery of novel LNP chemistries Collaborate with computational scientists to identify design pathways for LNPs that achieve targeted functional properties (e.g., potency, tissue tropism, tolerability) and guide their synthesis Work with infrastructure and automation teams to streamline real-time data transfer between predictive models and experimental platforms Partner with experimental teams to drive iterative LNP design–make–test–analyze (DMTA) cycles, including development of domain-specific acquisition functions for multi-objective optimization Communicate findings to stakeholders and leadership through written reports and technical presentations Qualifications PhD in Computer Science, Applied Mathematics, Bioengineering, Chemical Engineering, or a related quantitative field with a strong ML focus 4+ years of ML-based in industry and/or academic settings Strong experience with uncertainty quantification, active learning, and Bayesian Optimization in drug delivery, materials science, or related fields Proficiency in ML frameworks (PyTorch/TensorFlow/JAX) and the Python data science ecosystem (NumPy, SciPy, Pandas, etc.) Hands-on experience with cloud computing (e.g., AWS, GCP, Azure) to accelerate model training and deployment Strong independent problem-solving ability and attention to detail Demonstrated achievement in industry or academia (publications, patents, or successful ML-driven projects) Excellent communication and presentation skills for both technical and interdisciplinary audiences Enthusiasm for working with cross-functional teams of experimentalists, engineers, and computational scientists in a fast-paced, entrepreneurial environment Preferred Qualifications Experience with AWS services for ML training, workflow orchestration, and data management Familiarity with integration of ML models into experimental workflows Background in drug delivery, lipid chemistry, or nanoparticle formulation 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. 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.

Responsibilities

The candidate will design, build, and scale supervised ML models for optimizing lipid nanoparticle formulations and in vivo performance. They will collaborate with experimental teams to drive iterative design cycles and communicate findings to stakeholders.