Role overview
We are seeking a highly motivated Senior Machine Learning Engineer / Data Scientist to join our AI/ML team. This individual will play a central role in designing and implementing advanced AI/ML systems with a focus on Retrieval-Augmented Generation (RAG), graph-based RAG, large language models (LLMs), agentic orchestration, and conversational AI (chatbot) solutions. Working closely with the Head of AI/ML and cross-functional partners, you will build and optimize LLM-powered pipelines and multi-agent systems that integrate knowledge graphs, multi-omics data, and biological context to uncover disease-driving proteins and pathways. The insights generated will directly support therapeutic discovery and development.
Responsibilities
- Architect and implement scalable RAG and LLM-based systems that integrate multi-modal data sources, including knowledge graphs, documents, and structured biological datasets.
- Design and deploy RAG and graph-based RAG pipelines that leverage LLMs and knowledge graphs to retrieve, reason over, and synthesize complex biological information.
- Build and maintain agentic orchestration frameworks (multi-agent systems) that coordinate LLM-based agents for end-to-end scientific reasoning, data retrieval, and decision support.
- Collaborate with data engineering teams to design data pipelines that harmonize and prepare large-scale omics datasets for model training.
- Develop and optimize conversational AI (chatbot) interfaces that enable scientists and stakeholders to query, explore, and interact with internal data and model outputs using natural language.
- Partner with experimental scientists to ensure model outputs are biologically interpretable and experimentally testable.
- Stay abreast of advances in LLMs, RAG architectures, agentic AI, and conversational AI; bring innovative ideas into the team.
- Ph.D. in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field with 1–3 years of industry experience (preferred); or M.S. in a related field with 4–6 years of industry experience.
- Proven track record in building LLM-based applications, with hands-on expertise in RAG, graph-based RAG, agentic orchestration, and/or chatbot development.
- Proficiency in Python and LLM/ML frameworks such as LangChain, Hugging Face Transformers, PyTorch, or similar.
- Experience working with multi-omics or high-dimensional biological data is a plus
- Familiarity with probabilistic modeling, causal reasoning, or statistical inference is a plus.
- Strong experience with knowledge graph technologies, graph databases, and vector databases.
- Demonstrated ability to work in cross-disciplinary teams, communicate complex ideas clearly, and deliver results in fast-moving environments.
About the company
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.