ProFound Therapeutics: AI/ML Engineering - SAGE Platform Co-Op

Flagship Pioneering Co-Op Program • Cambridge, MA

1d • $25 - $35 • Hybrid

About The Position

ProFound Therapeutics is a biotechnology company that discovers novel proteins across the expanded human proteome to identify new medicines and drug targets. Its ProFoundry Platform combines protein detection, validation, and computational technologies to identify and characterize previously inaccessible proteins and assess their therapeutic potential.

The AI/ML team builds SAGE, an internal multi-agent LLM platform that helps scientists reason over knowledge graphs, biological databases, and scientific literature. The AI/ML Engineering Co-Op will extend SAGE’s capabilities and help ensure the platform performs reliably. The co-op will own features from design through deployment, working with ML engineers and scientists who use SAGE. The role provides hands-on experience building and operating a production AI system in a drug discovery setting.

The work spans agent tools and integrations, document ingestion, chat-interface collaboration features, LLM evaluation and benchmarking, and monitoring of usage, performance, reliability, and cost. Success includes deploying at least one new SAGE capability for scientists, establishing a regularly running evaluation and benchmarking pipeline, improving visibility into platform usage and performance, and producing documented work the team can maintain. The position is based in Watertown, Massachusetts, with a hybrid schedule, from January through June 2027. It is a full-time, 40-hour-per-week, short-term co-op engagement.

Requirements

• Currently pursuing a B.S. or M.S. in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Available for a full-time, 4–6 month co-op term.
• Strong Python skills.
• Hands-on experience building with LLMs, such as LLM APIs, RAG, agents, or MCP, through coursework, projects, or prior internships.
• Familiarity with Git and software engineering best practices, including testing, code review, and documentation.
• Curiosity, self-direction, and the ability to communicate clearly with technical and non-technical colleagues.

Nice To Haves

• Experience with web or backend development, such as FastAPI, React, or TypeScript.
• Experience with LLM evaluation frameworks or observability and tracing tools.
• Exposure to knowledge graphs, vector databases, or scientific and biological data sources.
• Familiarity with cloud environments such as AWS, containers such as Docker, and CI/CD tooling such as GitHub Actions.
• Prior internship or co-op experience in software or ML engineering.
• Interest in biology or drug discovery; a biology background is not required.

Responsibilities

• Design, build, and ship SAGE tools that connect the agent to external data sources, APIs, and MCP servers.
• Extend SAGE’s ability to ingest and reason over user-uploaded documents, including parsing, chunking, and retrieval.
• Contribute to agent orchestration and retrieval-augmented generation components spanning knowledge graphs, biological databases, and scientific literature.
• Build features for the SAGE chat interface that improve usability and collaboration, such as conversation sharing and source and citation display.
• Gather feedback from scientists and turn it into practical, prioritized improvements.
• Develop evaluation and benchmarking pipelines to measure answer quality, accuracy, and hallucination rate.
• Help build and curate benchmark datasets with scientists, using automated approaches such as LLM-as-judge and human review.
• Help integrate automated quality checks and regression tests into the CI/CD pipeline.
• Build monitoring for SAGE usage, performance, reliability, and LLM cost.
• Write clean, tested, and documented code and participate in code reviews.
• Present progress and results to the AI/ML team and scientific stakeholders, including a final co-op presentation.

Benefits

• Mentorship from experienced ML engineers.
• Access to Flagship Pioneering Co-Op Program enrichment sessions, a dedicated professional development track, and a mentor.
• Opportunity to be considered for future opportunities across Flagship and its portfolio companies.
• Compensation of $25–$35 per hour.
• Retirement benefits may be available after completing a set number of hours.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern