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.
Requirements
- 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.
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.
Other
- 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).
- Excellent communication skills, with the ability to convey complex technical insights to experimental biologists and drug discovery teams.
- We are seeking individuals with an entrepreneurial spirit, strong communication skills, and comfort working in and contributing to a dynamic and cross-functional team environment.
- The level of the role will be commensurate with the education and years of experience of the identified candidate.
- We are dedicated to building diverse and inclusive teams and encourage everyone to apply.
