AI Engineering Intern (unpaid)
ABOUT HEKAHeka is a stealth-stage startup building AI infrastructure for clinical referral workflows. We're early but real. We already have three clinics lined up for pilot deployments. We're hands-on, moving fast, and building the patterns for clinical AI that the rest of the industry hasn't written down yet.THE TEAMYou'll work directly with our founding team: a small group of Stanford, Harvard, and Cornell alumni with backgrounds spanning AI/ML engineering, quantitative research, and healthcare. Because we're small and early, an intern gets unusually direct access here, real code review, hands-on mentorship, and a front-row view of how an early-stage company actually gets built.ABOUT THE ROLEA mentorship-driven internship for a student who wants to learn how applied AI systems are actually built: structured, tested, observable, and safe enough to run in real healthcare settings. You'll work closely with the founding team, with direct mentorship and code review, on the AI systems behind our product. The goal is your learning: you'll leave with hands-on experience and a portfolio most students never get.WHAT YOU'LL WORK ON (with mentorship and review)You'll get hands-on across the AI stack behind our product:Structured LLM outputs using Pydantic, JSON Schema, and validation toolingDocument AI: extraction and classification pipelinesEval datasets and eval-driven prompt optimization (DSPy / GEPA-style)Workflow orchestration in TemporalAI observability: traces, confidence scores, human corrections, regression reportsTests and deployment tooling so AI changes can move safely toward productionTECH YOU'LL BE EXPOSED TOPython · TypeScript / Node.js · Pydantic · Zod / TypeBox · AWS Bedrock · Temporal · Postgres · S3 / SQS / Lambda / ECS · Docker · GitHub Actions · DSPy / GEPA-style optimization · OpenTelemetry / CloudWatch (You won't touch all of these; we'll pick what fits your projects.)WHAT WE'RE LOOKING FORA current undergraduate or graduate student in CS, Software Engineering, AI/ML, Data Science, or a related field, ideally able to take this for academic credit or through a co-op / work-integrated-learning program. You should have:Strong programming ability in Python, TypeScript, or bothExperience building projects outside of classInterest in LLMs, AI agents, evals, document processing, or workflow automationComfort with APIs, databases, and backend systemsGood judgment around privacy, reliability, and correctnessExcitement about applying AI to healthcareYou do NOT need prior healthcare or full-time engineering experience.NICE TO HAVELLM / RAG / agent / ML projects · FastAPI or typed-validation tools · cloud exposure (AWS/GCP/Azure) · Docker / CI/CD · OCR, PDFs, or data pipelines · coursework in ML, NLP, databases, distributed systems, or algorithms.WHAT YOU'LL GAINDirect mentorship from the founding team, with regular code reviewHands-on experience with industry-standard applied AI in a real clinical settingA concrete portfolio you can talk through in interviewsA letter of recommendation and a reference on successful completion