Machine Learning Engineer, Information Security

Join Apple’s Information Security Machine Learning (ISML) team, where we are

redefining cybersecurity through data-driven intelligence. Our mission is to

transform traditional reactive security measures into autonomous systems that

proactively detect and defend against threats. We achieve this through

cutting-edge research, applied science, and robust infrastructure development.

We are seeking a highly motivated and talented Machine Learning Engineer to join

our dynamic and growing team. You will play a pivotal role in designing,

developing, and deploying machine learning models that power our advanced

security products and services. This is an incredible opportunity to make a real

world impact by building intelligent systems that detect and prevent advanced

threats, enhance critical security processes, and protect Apple and our

customers. We're looking for a passionate and highly skilled macOS engineer to

join our team and build the foundation for autonomous security on Apple devices.

This role requires a deep understanding of the macOS environment and a proven

ability to develop and deploy high-performance applications.

DESCRIPTION

The Security ML Engineer will bring their expertise in machine learning to the

problems and opportunities facing Information Security at Apple. You will

contribute to the Autonomous Security program by developing production ready

AI/ML systems using Apple’s internal platforms, cloud services, and local

compute environments. You will translate research to design, building and

deploying machine learning models for security use cases, leveraging generative

AI, statistical modeling, reinforcement learning, and data science to address

complex security challenges. You will collaborate with cross-functional teams

including security teams, software engineers, and researchers to prototype and

scale AI/ML driven security solutions. You will own end-to-end ML workflows:

data exploration, model development, evaluation metrics design, deployment, and

monitoring.

MINIMUM QUALIFICATIONS

BSc or Masters degree in Machine Learning, Data Science, Computer Science,

Information Security, Mathematics, Statistics, or related field. Strong

programming skills in Python and Scala; experience with ML libraries such as

TensorFlow, PyTorch, HuggingFace, and Scikit-learn. Hands-on experience with

full ML model lifecycle: from experimentation to deployment and monitoring.

Solid grasp of security fundamentals including network security, incident

response, threat modeling, and vulnerability management. Excellent written and

verbal communication skills, with the ability to present technical concepts

clearly to varied audiences. Familiarity with CI/CD workflows and ML pipelines .

Experience operating, and scaling production services in cloud native

environments. Experience deploying models on CUDA devices using tools like

TensorFlow or Torch. Proven experience building generative AI applications for

real-world use cases.

PREFERRED QUALIFICATIONS

Ph.D. in a technical field such as Computer Science, Engineering, Statistics, or

related disciplines. In-depth knowledge of ML algorithms, including

supervised/unsupervised learning, deep learning (CNNs, RNNs, LSTMs), and large

language models. Industry experience in deploying ML and generative AI solutions

in cybersecurity contexts. Familiarity with cloud platforms (e.g., AWS, GCP) and

their security offerings is a plus. Experience with large scale data processing

and analysis using tools such as Apache Spark. Experience working in a key

security process, such as Incident Response, Threat Intelligence, or

Vulnerability Management. Experience with specific security tools and

technologies (e.g., SIEM, IDS/IPS, endpoint security solutions). Contributions

to open-source security or machine learning projects. Publications or talks at

top-tier ML or security conferences. Additional proficiency in C++ or Swift is a

plus