A.I. Engineering Intern/Fellow
A.I. Engineering Intern/Fellow
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Job Description
Our Culture
At Manifold we value intellectual rigor humility and mission-driven collaboration. We believe that technology is only as powerful as the people behind it and were building a culture that supports growth inclusion and curiosity. We work fast think deeply and strive to make a lasting impact on patients lives.
About Manifold
As the amount of biomedical data types and scale continues to grow old ways of working with data hold back the pace of progress fragmented data overwhelming omics complex manual work analysis backlogs friction in secure collaboration and barriers to distributing workflows.
Manifold is a health research infrastructure company that enables researchers to focus on the high-impact research that matters most by taking care of all the other stuff that gets in the way. As a trusted research platform for modern scientific collaboration we partner with top-tier healthcare providers life sciences organizations and research institutions to unlock real-world data and deliver actionable insights that improve precision medicine and discovery.
Were a early stage VC funded startup with over 50 employees across North America backed by world-class investors and healthcare leaders including Andrew Marks of TQ Ventures Jason Schoettler of Calibrate Ventures Gaurav Singal of Foundation Medicine Steve Fredette of Toast Amar Kendale of Livongo Andrew Bialecki of Klaviyo Brad Porter of Amazon Jon Karlen of Acadian Software and Pravin Soni of Elevation Pharma.
We are seeking an AI Engineering Intern/Fellow who is excited about building production-grade AI systems and Agents that meet customer needs.
In this role you will be working with our Data and AI team. You will experiment with new Agentic Design ideas build and integrate MCP servers add new developer frameworks and integrate foundational models into our platform. You will set up processes for continuous evaluation of AI features using evaluation datasets. Your work will impact the product features in Manifold directly.
What Youll Do
- Integration of LLM developer frameworks (For example Pydantic AI Semantic Kernel Crew AI etc.).
- Work on internal infrastructure for RAG pipelines that power our products AI agents.
- Add evaluation datasets and develop evaluation methods.
- Assist in bringing these improvements into the Manifold product offering (developer-facing SDKs agent orchestration or compliance-ready AI workflows).
- Document and share findings helping shape the future of development culture at Manifold.
About You
- Currently pursuing a BS/MS/PhD in Computer Science Engineering or a related technical field.
- Strong programming skills in Python C/C Go or similar
- Familiarity with CI/CD systems (GitHub Actions CircleCI Argo etc.) and cloud platforms (AWS GCP).
- Interest or experience with:
- Foundational models and LLM frameworks (Examples: HuggingFace OpenAI/Anthropic APIs).
- Infrastructure for RAG vector databases (Pinecone Weaviate FAISS) embeddings pipelines and knowledge orchestration.
- Strong coding skills with a continuous learning mindset to work with senior technical team and ship features in production
Why Join Manifold as an Intern
- Work on the frontier of AI engineering culturenot just building prototypes but shipping AI features in production
- Exposure to foundational models LLM developer frameworks and AI best practices in a real-world enterprise setting.
- High-impact projects that directly influence our customer-facing platform features.
- Join a world-class team with backgrounds from MIT Harvard Google Qualcomm Kyruus and leading biotech startups.
The Opportunity
At Manifold interns are treated as full contributors. Youll gain hands-on experience in shipping production-grade code while shaping how LLM frameworks and Agentic designs are embedded into the platform. This is an opportunity to develop innovative software and AI systems that accelerate discoveries ultimately impacting patient outcomes worldwide.
Required Experience:
Intern
Employment Type
Intern
