Associate Director / Director, AI & Machine Learning

Flagship Pioneering • Boston, MA

84d

About The Position

ProFound Therapeutics is pioneering the discovery of the expanded human proteome to unlock a new universe of potential therapeutics. By integrating multi-omics, advanced computation, and translational biology, we aim to reveal and characterize thousands of previously uncharted proteins and systematically explore their role in health and disease. We are seeking an experienced Associate Director / Director of AI & Machine Learning to join our growing team. This individual will play a key leadership role in shaping and executing our AI/ML strategy while remaining actively involved in hands-on model development and implementation. Reporting directly to our CTO, the successful candidate will lead the design and deployment of advanced machine learning systems - including generative and transformer-based models (LLMs), graph neural networks, and causal inference methods - within a multi-agent causal AI framework. These efforts will directly support the discovery of disease-driving proteins and pathways, advancing ProFound's mission to accelerate therapeutic development.

Requirements

• Ph.D. or M.S. in Computer Science, Computational Biology, Biostatistics, Applied Mathematics, or related field, with 7+ years of relevant post-graduate or industry experience (biotech, pharma, or AI/ML research).
• Demonstrated expertise in transformer architectures, LLMs, graph neural networks, and generative modeling.
• Strong background in causal inference and probabilistic modeling, with practical experience applying DAG-based or counterfactual methods.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, JAX, or PyTorch Geometric.
• Experience working with multi-omics or high-dimensional biological data strongly preferred.
• Proven ability to balance strategic leadership with hands-on development and deployment of advanced ML models.
• Familiarity with knowledge graph technologies and graph databases is a plus.
• Experience with computational imaging or digital pathology data integration is a strong plus.
• Excellent communication skills, with the ability to convey complex technical insights to experimental biologists and drug discovery teams.

Nice To Haves

• Familiarity with knowledge graph technologies and graph databases.
• Experience with computational imaging or digital pathology data integration.

Responsibilities

• Provide technical leadership while remaining hands-on in developing, training, and deploying AI/ML models.
• Architect and implement scalable ML systems that integrate multi-modal data (genomics, transcriptomics, proteomics, imaging, digital pathology, perturbation data).
• Lead the development of graph-based, transformer-based, and generative models (including LLMs and multi-modal transformers for biological and imaging data) to capture biological relationships and simulate interventions.
• Drive the creation of a multi-agent causal AI framework that integrates causal graph learning, interventional simulation, and knowledge graph reasoning.
• Collaborate with data engineering teams to design robust pipelines that harmonize and prepare large-scale omics datasets for model training.
• Implement, evaluate, and optimize causal inference approaches (e.g., DAG learning, treatment-effect estimation, counterfactual modeling).
• Partner closely with experimental scientists to ensure model outputs are biologically interpretable and experimentally testable.
• Mentor and develop junior AI/ML team members, fostering technical excellence and cross-disciplinary collaboration.
• Stay current with emerging advances in AI/ML, causal modeling, and computational biology; proactively introduce new approaches that strengthen the platform.

What This Job Offers

• Career Level: Senior
• Industry: Securities, Commodity Contracts, and Other Financial Investments and Related Activities
• Education Level: Ph.D. or professional degree
• Number of Employees: 251-500 employees