Principal Scientist, Machine Learning, Origination

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Company Overview
Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures. Leveraging recent advances in AI, machine learning, and data, the organization accelerates fundamental research and creates a portfolio of AI-first companies. Operating within Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating AI advancements across human health, sustainability, and other domains.

Summary
The Principal Scientist will lead multiple AI/ML or computational projects across early-stage ventures as part of the company origination process. This role involves defining AI strategies, overseeing method and platform development—including systems design, drug design, molecular modeling, systems biology, protein design, and LLM/agentic workflows—and ensuring rigor in model development, benchmarking, scaling, and reporting. The position includes team leadership, cross-functional collaboration, and external representation.

Key Responsibilities

• Program Leadership: Lead development, implementation, control, and reporting of multiple AI/ML or computational projects in alignment with strategic plans, budgets, and timelines.
• Technical Ownership: Drive method and pipeline development, LLM-based agent/workflow design, benchmarking, scaling, and production implementation.
• Best Practices: Promote operational excellence in AI projects and educate cross-functional collaborators.
• Team Leadership: Manage and coordinate internal and external scientists/engineers; mentor early hires; support recruiting and interviews.
• Planning & Resourcing: Contribute to project planning, budgets, resources, and timelines; identify risks and tradeoffs with clear options.
• Landscape & Strategy: Scout emerging literature and AI/ML advancements; propose new strategies and identify opportunities for ventures.
• Representation & Community: Act as a subject matter expert representing the organization to portfolio companies and external partners; participate in scientific conferences and meetings.
• Communication & Influence: Adapt and present complex findings to diverse audiences; influence project direction and technical approaches.

Professional Experience & Qualifications

• Master’s or PhD in relevant fields (machine learning, mathematics, statistics, computational sciences) with 5+ years of scientific/engineering/computational experience; industry AI/ML experience preferred.
• Proven success driving results directly or indirectly through teams in fast-paced, entrepreneurial, technical environments.
• Track record of independent thought and creativity driving high-impact, cross-disciplinary AI/ML projects.
• Leadership experience contributing to decisions on AI/ML model progression.
• Expertise with Python, modern ML frameworks (PyTorch, JAX, TensorFlow), version control, databases, deep learning architectures, and relevant informatics tools.

Preferred Qualifications

• Breadth across domains such as protein modeling/design, proteomics/mass spec, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation.
• MLOps expertise: data contracts and lineage (DVC/LakeFS), experiment tracking (MLflow/W&B), secure AWS infrastructure (S3, Batch/ECS/EKS, SageMaker), Docker, IaC (Terraform/CDK), CI/CD (GitHub Actions).
• Experience with generative modeling (diffusion/flow/VAEs), docking rescoring (gnina, DiffDock), workflow orchestration (Airflow, Prefect, Argo), data warehouses (Redshift, Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit, Gradio).
• Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups.

Why Pioneering Intelligence

• Operate at the frontier: Build and deploy AI/ML that powers discovery across multiple new ventures.
• Own the full stack: Ship end-to-end systems from scoping and data contracts to models, MLOps, and internal UIs.
• Compound impact: Convert one-off wins into shared libraries and templates adopted portfolio-wide.
• Work with founders: Partner closely with venture leadership and platform engineers; influence strategy through hands-on delivery.
• Grow fast: Stretch across domains, take situational leadership, present at conferences, and help shape initial ML teams.

Disclaimer
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