Senior Director / Vice President, Machine Learning (Generative Chemistry)

Flagship Pioneering, Inc.•Cambridge, MA

46d•$279,000 - $412,500

About The Position

FL117, a venture-back stealth AI x bio startup, is seeking a highly skilled Sr. Director/VP ML, Generative Chemistry. Here you will lead AI/ML and cheminformatics projects building the next-gen multi-modal generative models for biomolecular design. You will define and deliver pragmatic AI strategies, oversee method and platform development (ex: systems design, drug design, molecular modeling, LLM/agentic workflows), and ensure rigor in model development, benchmarking, and scaling. You will manage the computational team split across AI/ML, molecular simulations, and cheminformatics, directly influencing company building decisions. We are looking for independent leaders, excited to work on ambitious projects in a fast-paced environment to push the frontier of AI-powered drug discovery.

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

Nice To Haves

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 libraries 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.

Responsibilities

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.
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.
Promote operational excellence in AI projects by educating cross-functional collaborators.
Manage and/or coordinate internal scientists and engineers and cross functional 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 landscape; synthesize concepts to propose new development strategies and identify opportunities for PI and venture portfolios.