ProFound Therapeutics, Inc.
Biotechnology
SeniorMachineLearningEngineer/DataScientist
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“Senior Machine Learning Engineer / Data Scientist at ProFound Therapeutics, Inc.. Skills: Machine Learning, LLM, RAG, Agentic AI. Architect scalable RAG systems. Implement scalable RAG systems”
What You'll Achieve.
Uncover disease-driving proteins; Uncover disease-driving pathways; Support therapeutic discovery; Support development
Industry & Context.
Scientific reasoning
What They're Looking For.
Must Have
M.S. in a related field with 4–6 years of industry experience, Proven track record in building LLM-based applications, Hands-on expertise in RAG, Hands-on expertise in graph-based RAG, Hands-on expertise in agentic orchestration, Hands-on expertise in chatbot development, Proficiency in Python, Experience with knowledge graph technologies, Experience with graph databases, Experience with vector databases, Demonstrated ability to work in cross-disciplinary teams, Demonstrated ability to communicate complex ideas clearly, Demonstrated ability to deliver results in fast-moving environments
Nice to Have
Ph. D. in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or related field with 1–3 years of industry experience, Experience working with multi-omics or high-dimensional biological data, Familiarity with probabilistic modeling, Familiarity with causal reasoning, Familiarity with statistical inference
What You'll Do.
Architect scalable RAG systems
Implement scalable RAG systems
Architect LLM-based systems
Implement LLM-based systems
Integrate multi-modal data sources
Design graph-based RAG pipelines
Deploy graph-based RAG pipelines
Leverage knowledge graphs
Retrieve biological information
Reason over biological information
Synthesize biological information
Build agentic orchestration frameworks
Maintain agentic orchestration frameworks
Coordinate LLM-based agents
Perform scientific reasoning
Design data pipelines
Prepare omics datasets
Develop conversational AI interfaces
Explore internal data
Interact with internal data
Partner with experimental scientists
Ensure model interpretability
Ensure models are experimentally testable
Stay abreast of advances in LLMs
Stay abreast of advances in RAG architectures
Stay abreast of advances in agentic AI
Stay abreast of advances in conversational AI
Bring innovative ideas into the team
How You'll Work.
Team & Collaboration
Cross-functional partners; Data engineering teams; Experimental scientists; Cross-disciplinary teams
Communication Scope
Communicate complex ideas
Full Job Description
About ProFound Therapeutics 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. The Role 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. Key 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 experi
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