Flagship Pioneering, Inc. - Senior Director / Vice President, Machine Learning (Generative Chemistry)
Requirements
• MSc or PhD in Computer Science, AI/ML, Physics, Biophysics, or a related field • 7+ years of experience scientific/engineering/computational in Pharma or Biotech settings; industry AI/ML experience preferred • Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fast paced, entrepreneurial, and technical environments. • Clear evidence of sustained independent thought and creativity driving high impact, across drug development and chemistry 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 • Proven ability to lead technical initiatives, communicate progress, and drive innovation in generative biomolecular design • Breadth across domains such as protein modeling/design, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation. Please send us your GitHub, if applicable. • Strong presence in the ML community through publications and conference proceedings. Please send a list of publications, if applicable. • MLOps expertise: data contracts and lineage (e.g., DVC/LakeFS), experiment tracking (MLflow/W&B), secure AWS infrastructure (S3, Batch/ECS/EKS, SageMaker), Docker, IaC (Terraform/CDK), and CI/CD (GitHub Actions). • Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics. • Workflow orchestration (Airflow/Prefect/Argo), data warehouses (Redshift/Snowflake), vector search (FAISS/pgvector), and lightweight internal tools (FastAPI, Streamlit/Gradio). • Familiarity with ML software development and cloud services (Docker, AWS Batch, Step Functions, EKS, AWS ParallelCluster, etc.) • Experience with librariers like: rdkit, rosetta, prody, biopython, pymol, openmm, dagster, prefect, etc. • Experience mentoring early hires, acting as interim Head of ML, and contributing to hiring plans and interview processes at startups. • 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.
Responsibilities
• Program Leadership: Lead development, implementation, control, and reporting of several AI/ML or computational projects within assigned ventures in line with broader strategic plans of PI and Flagship, budgets, and timelines. • Program Leadership • Technical Ownership: Take a specialized technical role on project teams to oversee method development, pipeline development, and LLM based agent/workflow design; drive benchmarking, scaling, and implementation into production grade systems. • Technical Ownership • Best Practices: Promote operational excellence in AI projects by educating cross-functional collaborators. • Best Practices: • Team Leadership: Manage and/or coordinate internal scientists and engineers and cross functional project teams as applicable; mentor early hires; support recruiting and interview. • Team Leadership • Planning & Resourcing: Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options. • Planning & Resourcing • Landscape & Strategy: Independently scout emerging literature and the AI/ML landscape; synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios. • Landscape & Strategy
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