Apple, AI/ML Internship: Self-Improving AI Agents for Cybersecurity, SEAR

Security tasks are a distinctive setting for agentic self-improvement. (a) Success is highly verifiable but rare. (b) The search space is vast and the horizons are extremely long. (c) They are grounded in real-life codebases that constantly evolve. These properties make security both a demanding test and a rich source of signal for continuously improving agents automatically. While base models provide the basis of an agent's capability, the harness around them has a large effect on many of the required attributes.

The goal of this internship is to experiment with new self-improvement methods on real-world security problems. You will study how to automatically improve harnesses on cost, performance, robustness, and how to feed-in signals from the software development lifecycle into that process. Novel ideas will be required to tackle these challenges. Code and the agent's environment constantly evolve: fixes for past issues may not lead to future ones, and the harness must keep evolving. Because agents are non-deterministic and successes are sparse, telling signal from noise is both critical and hard. Solving these challenges calls for novel techniques that draw on state-of-the-art LLM research, active learning, black-box optimization, software engineering practice, and program analysis.

Minimum Qualifications

• Currently doing a degree in one of the following fields: machine learning, AI, software engineering, computer science, security, or a related field.
• Experience building, experimenting with, and evaluating agentic AI systems (e.g., with LangChain, LangGraph, OpenHands SDK, Pi).
• Strong software engineering skills in Python, and experience with agentic coding.
• Autonomous drive, a collaborative mindset, and a proactive approach to problem-solving.

Preferred Qualifications

• Experience with building and evaluating agentic software-related tasks (e.g., vulnerability research), agentic self-improvement, or memory.
• Knowledge of statistics and black-box optimization methods.