Build and deploy agentic AI workflows that automate repeatable, high-volume engineering processes such as CVE triage and support ticket management
Use the DataRobot platform — including AutoML, GenAI tooling, and MLOps capabilities — to design, evaluate, and ship solutions
Translate ambiguous team pain points into well-scoped AI/ML problems with defined success criteria
Work autonomously on defined project deliverables while staying aligned with your mentor and team
Communicate progress through structured weekly updates and a final Intern Showcase presentation to DataRobot leadership
Collaborate across engineering, product, and go-to-market teams to understand context and deliver work that sticks
Document your work thoroughly so findings and solutions can be handed off and extended after the program

Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field
Working proficiency in Python (pandas, numpy) and comfort with data manipulation and scripting
Foundational understanding of machine learning concepts — supervised learning, model evaluation, feature engineering
Deep passion for autonomous agents — you have experience building systems that "do" rather than just "chat"
Strong written and verbal communication skills; ability to explain technical work clearly to mixed audiences
Ability to work independently, manage ambiguity, and escalate blockers appropriately
Nice to Have:
Prior hands-on experience with LLMs, prompt engineering, or agentic frameworks (LangGraph, CrewAI, LlamaIndex)
Familiarity with the DataRobot platform or other MLOps/AutoML tools
Experience with API development (FastAPI, Flask) or containerization (Docker)
Prior internship, research, or project experience applying ML to real-world problems
Experience with or interest in DevSecOps concepts, CVE triage, or support engineering workflows.

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