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

About the role

FL117 is a venture‑backed stealth AI x bio startup seeking a Sr. Director/VP ML, Generative Chemistry. In this role you will lead AI/ML and cheminformatics projects to build next‑generation multi‑modal generative models for biomolecular design, define and deliver pragmatic AI strategies, oversee method and platform development (systems design, drug design, molecular modeling, LLM/agentic workflows), ensure rigor in model development, benchmarking, and scaling, and manage the computational team split across AI/ML, molecular simulations, and cheminformatics, directly influencing company building decisions.

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.
• 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.
• Best Practices: Promote operational excellence in AI projects by educating cross‑functional collaborators.
• Team Leadership: Manage and/or coordinate internal scientists and engineers and cross functional project teams as applicable; mentor early hires; support recruiting and interview.
• Planning & Resourcing: Contribute to project planning, including budgets, resources, and timelines; surface risks and tradeoffs early with clear options.
• 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.

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

Skills

• Breadth across domains such as protein modeling/design, cheminformatics/docking/ADMET, biophysics/MD, and LLM/agentic automation.
• 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