AI Engineer Intern

The Opportunity (Summer 2026 AI Internship - Applications Open Now)

We're seeking an AI Engineer Intern to work alongside our AI team on large-scale AI and Agentic  systems from data pipeline to production deployment. This role is scoped for someone with foundational experience who wants to deepen it: you'll own discrete pieces of real systems under the mentorship of senior engineers, not shadow work or isolated coursework-style projects.

What You'll Do

You’ll work directly with the AI team, taking responsibility for well-scoped pieces of real systems, with mentorship from senior engineers.

Benchmarks & Evaluation

• Contribute to APIFlow-Bench, our open-source benchmark for real API-development work: design and review benchmark tasks and their mock API environments, extend the evaluation harness and task-generation pipeline in Python, and help maintain the public multi-model leaderboard with statistical confidence intervals.
• Help build a new action-level AI safety benchmark: instead of grading what a model says, it scores what an agent actually does inside a simulated enterprise API environment. You’ll work on scenario design, threat modeling (prompt injection, data exfiltration, permission overreach), and auditable evaluation design.

Model Training & Efficiency

• Fine-tune open-weight models for tool calling and agentic tasks (SFT, distillation, and RL) using PyTorch and the open-source training ecosystem, on both managed training platforms and self-managed cloud GPUs.
• Design and run experiments with rigor: evaluate every training run on our benchmarks, support ablation studies and error analysis, track experiments, and report results honestly, including cost.
• Evaluate ultra-low-bit quantized models for on-device use: extend our quantized vs. full-precision benchmark comparisons and analyze where and why they diverge.

Agent Systems & Engineering Practice

• Help build the next generation of Postman’s in-product AI agent (Agent Mode): a deliberately minimal agent architecture that calls LLM APIs directly (tool loops, multi-step execution, checkpointing), primarily in TypeScript. No prior TypeScript is required; strong Python fundamentals transfer quickly.
• Read the source code of open-source agent harnesses and turn what you learn into design specs and prototypes.
• Document experiments, design decisions, and runbooks so your work is legible to the next person; flag safety, fairness, or privacy concerns you observe in model or agent behavior.

About You

• Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, or a related quantitative field.
• Hands-on experience training or evaluating ML models: course projects, research, hackathons, or a prior internship all count.
• Solid Python fundamentals: data structures, functions, basic testing; comfortable writing and reviewing code outside of notebooks.
• Working knowledge of at least one deep-learning framework (PyTorch preferred).
• Clear written and verbal communication, and a habit of documenting what you build.

Preferred Qualifications  (none required; the more of these you have, the better)

• Experience fine-tuning open-weight LLMs (SFT, LoRA, RL, or distillation), with the improvement measured on a benchmark.
• Experience building LLM agents (tool calling, multi-step loops) or LLM evaluation harnesses/benchmarks, and reporting results with statistical rigor.
• A track record of shipping real software end-to-end: APIs and services, CLIs, Docker, CI/CD, cloud; public code on GitHub is a big plus.
• Interest or experience in AI safety and robustness: red-teaming, prompt injection, agent security, fairness, or interpretability.
• Exposure to model-efficiency work: quantization, low-bit inference, or serving optimization.
• Evidence of rigor and initiative: publications, technical blog posts, ablation studies, or self-driven side projects with quantified results.
• Fluency with AI coding tools (Claude Code, Cursor, Codex) to ship fast while still deeply understanding the systems you build.