Machine Learning Engineer, Information Security

Join Apples 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 Apples internal platforms cloud services and local compute 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 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 written and verbal communication skills with the ability to present technical concepts clearly to varied with CI/CD workflows and ML pipelines .nExperience operating and scaling production services in cloud native deploying models on CUDA devices using tools like TensorFlow or experience building generative AI applications for real-world use cases.

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