AI Engineer, Intern

About the Role

Postman is seeking an AI Engineer Intern for Summer 2026 to work alongside the 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, owning discrete pieces of real systems under the mentorship of senior engineers.

Key Responsibilities

• Contribute to APIFlow-Bench, an 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.
• Maintain the public multi-model leaderboard with statistical confidence intervals.
• Build a new action-level AI safety benchmark, scoring agent actions within a simulated enterprise API environment:
• Work on scenario design, threat modeling (prompt injection, data exfiltration, permission overreach).
• Design auditable evaluations.
• Fine-tune open-weight models for tool calling and agentic tasks (SFT, distillation, and RL) using PyTorch and the open-source training ecosystem on managed training platforms and self-managed cloud GPUs.
• Design and run experiments with rigor:
• Evaluate every training run on 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 quantized vs. full-precision benchmark comparisons.
• Analyze divergence points.
• Build the next generation of Postman’s in-product AI agent (Agent Mode) in primarily TypeScript:
• Work with a minimal agent architecture that calls LLM APIs directly (tool loops, multi-step execution, checkpointing). No prior TypeScript required; strong Python fundamentals transfer quickly.
• Read the source code of open-source agent harnesses and translate learnings into design specs and prototypes.
• Document experiments, design decisions, and runbooks.
• Flag safety, fairness, or privacy concerns observed in model or agent behavior.

Requirements & Tech Stack

• Foundational experience in AI and Agentic systems.
• Experience with Python.
• Working knowledge of at least one deep-learning framework (PyTorch preferred).
• TypeScript (for Agent Mode development, but no prior experience required).
• PyTorch.
• LLM APIs.
• APIs.
• GPUs.
• SFT, distillation, RL (for fine-tuning).
• Tool calling, multi-step execution, checkpointing (for Agent Mode).
• Prompt injection, data exfiltration, permission overreach (for threat modeling).

Qualifications

• 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 prior internships count).
• Solid Python fundamentals: data structures, functions, basic testing; comfortable writing and reviewing code outside of notebooks.
• Clear written and verbal communication.
• Habit of documenting built systems.

Preferred Qualifications

• Experience fine-tuning open-weight LLMs (SFT, LoRA, RL, or distillation) with measured improvement on a benchmark.
• Experience building LLM agents (tool calling, multi-step loops) or LLM evaluation harnesses/benchmarks, and reporting results with statistical rigor.
• Track record of shipping real software end-to-end: APIs and services, CLIs, Docker, CI/CD, cloud; public code on GitHub is a 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).