Summary We are seeking a highly experienced Machine Learning Engineer to build, deploy, and optimize Large Language Model (LLM)-based applications, with a strong emphasis on MLOps/LLMOps and scalable production systems. At Apple, we believe in creating technology that enriches lives and empowers creativity. You'll play a pivotal role in developing Apple Intelligence, driving the next generation of groundbreaking products across all Apple platforms.

The team is a growing group that works closely with product, ML research, Data Science and infrastructure teams to ensure the successful delivery of Apple Foundation models and Apple Intelligence evaluations. We are looking for a Machine Learning Engineer focusing on MLOps/LLMOps infrastructure to build a next-generation LLM-powered evaluation system. In this role, you will scale our internal evaluation platform, build automation and self-service tools, and ensure the reliability and efficiency of large-scale LLM services, creating huge impacts across all AI products through innovations.

• Explore, design and implement advanced ML infrastructure framework and tools.
• Establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
• Ensure LLM services are scalable, efficient, and secure for high-traffic LLM services.
• Champion model observability, incident response, prompt versioning, and feedback loops.
• Use ingenuity and creativity to resolve complicated and/or novel product and engineering challenges.
• Implement processes and frameworks for continuous quality improvement of Apple Intelligence, fostering excellence and reliability.
• Work closely with data scientists, Front End engineers, product managers, and other stakeholders to define metrics, gather requirements, and deliver impactful solutions.

• 4+ years in software engineering with experience in large-scale software system design and implementation.
• Proven track record of shipping production-grade ML/LLM systems.
• Strong understanding of LLMs, fine-tuning, prompt engineering, vector databases and RAG patterns.
• Experience with distributed systems, databases (SQL/NoSQL), cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes).
• Ability to tackle complex challenges, think critically, and deliver innovative solutions.
• Excellent communication skills and a team-oriented attitude, thriving in a collaborative and fast-paced environment.
• Bachelor's degree in Computer Science, Engineering, or a related field.

• Hands-on experience with observability and evaluation tools for LLMs.
• Solid understanding of machine learning algorithms, model evaluation metrics, and data processing pipelines.
• Previous experience in a high-growth tech company or similar environment.
• Active participation in open-source projects related to AI/ML or Back End development.
• Master or Ph.D. in a related field.

At Apple, base pay is one part of our total compensation package and is determined within a range. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.