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.

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.\nYou 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.

BSc or Masters degree in Machine Learning, Data Science, Computer Science, Information Security, Mathematics, Statistics, or related field.\nStrong programming skills in Python and Scala; experience with ML libraries such as TensorFlow, PyTorch, HuggingFace, and Scikit-learn. \nHands-on experience with full ML model lifecycle: from experimentation to deployment and monitoring. \nSolid grasp of security fundamentals including network security, incident response, threat modeling, and vulnerability management.\nExcellent written and verbal communication skills, with the ability to present technical concepts clearly to varied audiences.\nFamiliarity with CI/CD workflows and ML pipelines .\nExperience operating, and scaling production services in cloud native environments.\nExperience deploying models on CUDA devices using tools like TensorFlow or Torch.\nProven experience building generative AI applications for real-world use cases.

Ph.D. in a technical field such as Computer Science, Engineering, Statistics, or related disciplines.\nIn-depth knowledge of ML algorithms, including supervised/unsupervised learning, deep learning (CNNs, RNNs, LSTMs), and large language models.\nIndustry experience in deploying ML and generative AI solutions in cybersecurity contexts.\nFamiliarity with cloud platforms (e.g., AWS, Google Cloud Platform) and their security offerings is a plus.\nExperience with large scale data processing and analysis using tools such as Apache Spark.\nExperience working in a key security process, such as Incident Response, Threat Intelligence, or Vulnerability Management.\nExperience with specific security tools and technologies (e.g., SIEM, IDS/IPS, endpoint security solutions).\nContributions to open-source security or machine learning projects.\nPublications or talks at top-tier ML or security conferences.\nAdditional proficiency in C++ or Swift is a plus