As a Machine Learning Engineer on our team, you will dive into a variety of unique problems that directly impact Apple Products.
Build & Innovate: Research and develop state-of-the-art AI/ML solutions and tools that transform how we train, deploy, and monitor models that power features in Apple Products.
Scale & Secure: Work at the intersection of scale and privacy. You will design secure, private, and highly performant systems, ensuring our systems respect and protect user privacy as a core engineering feature.
Collaborate & Influence: Act as a technical leader. You will collaborate with broader teams across Apple, actively participating in design discussions, giving and receiving constructive feedback, and mentoring junior engineers.
Champion Quality: Participate in our highly collaborative environment to elevate our engineering standards. From design reviews and rigorous code reviews to robust test automation, you will ensure our AI/ML systems are maintainable and resilient.
Minimum Qualifications
PhD or Master’s degree in Machine Learning, Computer Science, Computer Engineering, or equivalent practical experience.
Core Experience: 5 - 7+ years of professional software engineering experience with a strong focus on AI/ML engineering.
Engineering Foundations: Robust engineering design skills with expert proficiency in Python and/or Java/Scala. Deep understanding of computer science fundamentals like data structures, algorithms, and system design.
AI/ML Foundations: Solid understanding of traditional machine learning algorithms (supervised, unsupervised, semi-supervised) and cutting edge genAI technologies. Hands-on experience with modern AI/ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Distributed Systems & Big Data: Proven experience with large-scale distributed data processing technologies such as Ray, Spark, or modern alternatives.
Preferred Qualifications
AI/ML Infrastructure & Ops: Experience developing and maintaining large-scale ML infrastructure (services, pipelines, frameworks, and deployment tooling like Ray, Kubernetes, MLflow) is a plus
AI-Assisted Engineering: Proven proficiency in leveraging advanced Generative AI and LLM assistants (e.g., Claude, Gemini, etc) as force-multipliers across the software development lifecycle with high quality always in mind.
Soft Skills & Autonomy: Strong communication skills for requirement clarifications and cross-functional collaboration. Ability to lead with a high level of autonomy, driving critical initiatives from concept to production.