Job description

This role focuses on applied security research for production ML systems, with an emphasis on agentic and tool-using models deployed at scale. You will lead research efforts that surface real security risks in shipped or near-shipped systems, and you will drive mitigations that integrate cleanly into Apple’s ML platforms and products. You will operate at the boundary between research, platform engineering, and product security, conducting original research grounded in real system behavior and translating it into concrete design changes, launch requirements, and long-term hardening strategies. Impact is measured by risk reduction in production, not theoretical results alone. Ph.D. or equivalent experience in machine learning, security, systems, or a related field. Demonstrated experience in applied ML security, adversarial ML, or systems security with real-world impact. Strong experimental and engineering skills, with an emphasis on reproducibility and operational relevance. Experience researching or securing LLM-based or tool-augmented ML systems. Ability to work fluidly across research, engineering, and security review processes. Track record of influencing production systems through research-driven insights. Publications in top venues are a plus, but production impact is the primary signal.

Requirements

Must-haves

Ph.D. or equivalent experience in machine learning, security, systems, or a related field.
Demonstrated experience in applied ML security, adversarial ML, or systems security with real-world impact.
Strong experimental and engineering skills, with an emphasis on reproducibility and operational relevance.
Experience researching or securing LLM-based or tool-augmented ML systems.
Ability to work fluidly across research, engineering, and security review processes.
Track record of influencing production systems through research-driven insights.

Nice-to-haves

Publications in top venues are a plus, but production impact is the primary signal.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningllm

Platform: apple
Effort: medium