Research Scientist, Applied Machine Learning Security (Agent Systems), SEAR
Generate a McCoy IQ challenge in 30 seconds.
See how candidates think and approach the work this role demands, before the phone screen. We'll build a video challenge from this posting, and you can edit or share it before it goes live.
Key details
What makes this role novel
Security research specifically focused on agentic ML systems—tool-using models that act autonomously—is a distinct discipline that emerged only in the last 2–3 years as LLM agents moved from research to production. The work of identifying vulnerabilities in agent behavior, designing adversarial evaluations for agent-specific threats, and hardening agentic design at scale did not exist as a job category before 2022–2023.
Job Description
At Apple, we believe privacy is a fundamental human right. Our Security Engineering & Architecture (SEAR) organization is at the forefront of protecting billions of users worldwide, building security into every product, service, and experience we create. The SEAR ML Security Engineering team combines cutting-edge machine learning with world-class security engineering to defend against evolving threats at unprecedented scale. We're responsible for developing intelligent security systems for Apple Intelligence that protect Apple's ecosystem while preserving the privacy our users expect and deserve. We're seeking a ML Security Research Scientist who operates at the intersection of applied research and production impact. You'll lead original security research on agentic ML systems deployed at scale—driving secure agentic design directly into shipping products, identifying real vulnerabilities in tool-using models and designing adversarial evaluations that reflect actual attacker behavior. You'll work at the boundary between research, platform engineering, and product security, translating findings into architectural decisions, launch requirements, and long-term hardening strategies that protect billions of users. Your impact will be measured by risk reduction in production systems that ship.
Audit details(provenance, verification trail, raw fields)
Core fields
apple:200681653-0836Provenance
appleVerification trail
This posting hasn't been probed by our closure verifier yet. Stream C runs on a rolling schedule against postings approaching the close-decision threshold.
LLM enrichment
See how we measure for definitions, or our corrections log for known issues. Found something wrong? Flag a correction.
