About The Position
We are looking for two highly motivated undergraduate interns to join our team working on next-generation Agentic AI platforms and neural-symbolic systems in the healthcare domain. This is not a passive internship. You will work on real systems that support complex, high-stakes use cases where reasoning, compliance, and explainability matter. You’ll collaborate closely with engineers, data scientists, and product teams to help shape how AI is deployed in production environments.
Requirements
- Currently pursuing a Bachelor’s degree (Freshman–Senior) in related field
- Hands-on experience with Python or similar programming languages
- Familiarity with LLMs / GenAI (e.g., building projects with OpenAI, Claude, Gemini, or open-source models)
- Strong curiosity and willingness to learn quickly in ambiguous environments
- Ability to break down problems and iterate through experimentation
- Communicating with others to exchange information.
- Problem-solving and thinking critically.
- Completing tasks independently.
- Interpreting data.
- Making timely decisions in the context of a workflow.
- Maintaining focus.
- Assessing the accuracy, neatness and thoroughness of the work assigned.
- Learning new tasks and completing tasks in situations that have a speed or productivity quota.
- Remembering and adhering to processes and protocols.
- Remaining in a stationary position, often standing or sitting for prolonged periods.
- Repeating motions that may include the wrists, hands, and/or fingers.
- Must be able to provide high-speed internet access/connectivity and office setup and maintenance.
- Must be able to provide a dedicated, secure work area.
Responsibilities
- Design and run evaluations for LLM-based agents (quality, reliability, safety)
- Analyze agent behavior and identify failure modes
- Propose improvements based on empirical results
- Build and enhance components such as tools, skills, memory systems, and MCP integrations
- Improve orchestration, observability, and developer experience
- Prototype new capabilities for real-world workflows
- Explore approaches combining LLMs with rules, knowledge graphs, or symbolic reasoning
- Work on use cases requiring auditability and explainability (e.g., claims, policy logic)
- Help bridge probabilistic AI with deterministic systems
- Partner closely with internal stakeholders or pilot users
- Rapidly prototype and iterate on solutions in real-world contexts
- Help define patterns for scaling adoption of agentic systems
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What This Job Offers
Career Level
Intern
