Principal Scientist, Machine Learning

Flagship Pioneering, Inc.•Cambridge, MA

4h•$208,000 - $286,000

About The Position

We are seeking a Principal Scientist (Embedded ML/Computational) to lead AI/ML and computational projects that accelerate pharmaceutical R&D across the preclinical, translational, and clinical continuum, working closely with Pioneering Medicines (PM) and across Flagship’s platform companies as part of the company origination process. You will define and deliver pragmatic AI strategies and oversee method and platform development across omics, biomolecule design, multi-o, systems biology, and scientific literature mining, ensuring rigor in model development, benchmarking, scaling, and reporting. Throughout, you will use LLM-based agents and agentic workflows as the connective tissue that ties these methods into end-to-end pipelines scientists rely on. You will manage cross functional contributors as applicable, influence company direction, and represent PI to venture teams, PM, and external partners. The ideal candidate is a self-directed serial deep diver - someone who can move from protein design one week to multi-omics or docking pipelines the next, and wire them together with agents that automate scientific workflows.

Requirements

Master’s, or PhD in a relevant field (e.g., machine learning, mathematics, statistics, computational sciences) with 5+ years' experience scientific/engineering/computational in academic, pharmaceutical, or biotechnology settings; industry AI/ML experience preferred.
Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments.
Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects.
Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs.
Depth across multiple core tools and concepts, including Python; modern ML frameworks (PyTorch or JAX/TensorFlow); version control; databases; deep learning architectures; and relevant informatics software.
Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable

Nice To Haves

Breadth across domains such as genomics, protein modeling/design, proteomics/mass spec, multi-omics, cheminformatics/docking/ADMET, biophysics/MD, and scientific literature mining, with the ability to connect them using LLM-based agents and agentic workflows.
Hands-on experience with agentic AI and orchestration frameworks (e.g., Pydantic AI, LangGraph, LangChain, CrewAI, AutoGen).
Experience integrating LLMs and model platforms, including Anthropic, OpenAI, Vertex AI, and Amazon Bedrock, and building evaluation and feedback-loop frameworks for agentic systems.
Familiarity with sandboxed code execution and agent harness development.
Experience with LLM observability and monitoring tooling and cost-governance / FinOps for AI infrastructure.

Responsibilities

Lead development, implementation, control, and reporting of several AI/ML and computational projects within assigned ventures and PM programs across the preclinical, translational, and clinical continuum, in line with broader strategic plans of PI and Flagship.
Own the build, scaling, benchmarking and maintenance of agentic systems that combine ML and computational biology tools (genomics,biomolecule design,literature mining,system biology, etc) which serve as end-toend systems for accelerating preclinical, translational and clinical R&D.
Promote operational excellence in AI projects by educating cross-functional collaborators.
Manage and/or coordinate internal and external scientists/engineers and crossfunctional project teams as applicable; mentor early hires; support recruiting and interview.
Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options.
Independently scout emerging literature and the AI/ML and agentic-AI landscape; synthesize concepts to propose new development strategies and identify opportunities to accelerate R&D across the preclinical, translational, and clinical continuum for PI, PM, and venture portfolios.
Influence the course of projects and technical approaches; adapt and present complex findings to diverse audiences to support meaningful interpretation and action.