Machine Learning Engineer

About The Company
Apple Inc. is a globally renowned technology company dedicated to designing, manufacturing, and marketing innovative consumer electronics, software, and services. Known for its commitment to quality, security, and user privacy, Apple has established itself as a leader in the industry with a diverse portfolio that includes iPhone, iPad, Mac, Apple Watch, and a suite of software platforms. The company emphasizes innovation, sustainability, and creating products that enhance the daily lives of its users worldwide. Apple’s Security Engineering & Architecture organization plays a critical role in safeguarding its ecosystem, ensuring that all products and services remain secure against evolving threats and vulnerabilities.

About The Role
We are seeking a highly skilled Applied Machine Learning Engineer to join our Security Engineering & Architecture team. In this role, you will work at the intersection of machine learning and security research to develop advanced, ML-enhanced systems that help identify vulnerabilities and strengthen the security posture of Apple’s products. Your primary focus will be on designing and implementing innovative solutions that analyze large and complex codebases, from custom silicon and firmware to user applications, leveraging cutting-edge ML techniques such as large language models, generative modeling, and agentic workflows. You will collaborate closely with security researchers and engineers, applying your expertise to create scalable, practical tools that facilitate vulnerability discovery and threat mitigation across all layers of Apple’s platforms. Your contributions will directly impact the security of billions of devices, helping to defend against sophisticated adversaries and ensuring the integrity of Apple’s ecosystem. Qualifications Expertise in machine learning, with a focus on large language models and generative modeling Strong enthusiasm for security, particularly offensive security techniques Proficiency in software engineering languages such as C, C++, Python, Swift, Objective-C, and Rust Experience or keen interest in security analysis, vulnerability research, and threat detection Excellent problem-solving, analytical, and collaborative skills Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience) Responsibilities Design and develop ML-enhanced security analysis systems to identify vulnerabilities in complex codebases Integrate ML models, including large language models and generative approaches, into security workflows Collaborate with security research teams to understand analysis challenges and develop scalable solutions Leverage raw data and expert insights to create tools that assist researchers in navigating intricate attack surfaces Apply techniques such as fuzzing, static and dynamic analysis, reverse engineering, and binary analysis to complement ML methods Participate in real-world security evaluations to validate and refine your innovations Stay current with advancements in ML, security research, and software analysis techniques to continuously improve systems Benefits Comprehensive health, dental, and vision insurance plans Generous paid time off and leave policies Retirement savings plans with company contributions Professional development opportunities and continuous learning support Employee wellness programs and resources Inclusive and diverse work environment fostering innovation and collaboration Equal Opportunity At Apple, we are committed to creating an inclusive environment where everyone is valued and respected. We are an equal opportunity employer and do not discriminate based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected status. We believe that diversity enhances our ability to innovate and serve our global community. We welcome applicants from all backgrounds and are dedicated to providing reasonable accommodations to support your application process and work environment.