AI Engineer (Internship) - Intelligent Question Bank Platform

Accel Learning•Secaucus, NJ

About The Position

Accel Learning is seeking an AI Engineer Intern to architect and implement the core AI pipeline for their test creation system. This role involves working closely with the founder to design and build an AI-powered content generation system from the ground up. Responsibilities include contributing to the entire product lifecycle, from data ingestion and understanding to output production and validation, as well as developing backend services and APIs. The intern will also focus on output quality, evaluation, and researching new AI tools and techniques. This is a generalist role at an early-stage product, requiring adaptability and direct input into development.

Requirements

Strong foundation in software engineering: data structures, APIs, system design.
Proficiency in Python.
Experience with REST APIs and at least one database (PostgreSQL preferred).
Ability to work independently, ask sharp questions, and iterate fast.
Strong debugging and problem-solving instincts.
Demonstrated side projects or shipped code (GitHub portfolio required).
Genuine interest in AI systems and education technology.
Direct experience with LLM APIs: OpenAI, Anthropic Claude, or Google Gemini.
Hands-on experience with RAG systems: embedding models, vector databases (Pinecone, Weaviate, pgvector, Chroma).
Familiarity with prompt engineering techniques: few-shot prompting, chain-of-thought, structured JSON outputs.
Experience with NLP pipelines: text chunking, tokenization, semantic search.
Knowledge of LaTeX syntax and math rendering libraries (MathJax, KaTeX).
Experience with image generation APIs or SVG programmatic generation.
Familiarity with AI evaluation frameworks or automated test harnesses for LLM outputs.
Cloud platform experience: AWS, GCP, or Vercel for deployment.
Experience with job queues: Celery, Bull, or similar.

Nice To Haves

Exposure to educational content standards or psychometrics is a bonus.

Responsibilities

Design and build an AI-powered content generation system from the ground up.
Contribute to all parts of the product end-to-end, including data ingestion, understanding source material, producing and validating outputs, and instructor interaction.
Build and iterate on LLM-driven pipelines.
Work with retrieval and embedding techniques to ground outputs in real source material.
Develop backend services and APIs to integrate the AI system.
Focus on output quality and build evaluation steps.
Identify and address failure modes in the system.
Improve the system based on real instructor feedback.
Research new tools and techniques in the AI space and integrate relevant ideas into the product.