Machine Learning Engineer - LLM

Machine Learning Engineer - LLM at Apple

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts.

Join Apple, and help us leave the world better than we found it.

Description

We are seeking a highly capable and dynamic Machine Learning Engineer to work cross-functional in building out a GenAI system in support of Beats and Apple Audio products. The ideal candidate thrives in ambiguity, is exceptionally organized, relentlessly detail-oriented, and an exceptional communicator at all levels of the organization.

Minimum Qualifications

• 3+ years experience in machine learning algorithms, software engineering, and data mining models with an emphasis on large language models (LLM) or large multimodal models (LMM).
• Masters in Machine Learning, Artificial intelligence, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field.

Preferred Qualifications

• Proven experience in GenAI application building with agents and agentic workflows.
• Experience with LLM and LMM development and fine-tuning.
• Experience applying ML techniques in manufacturing, testing, or hardware optimization.
• Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.
• Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale.
• Hands-on experience with LangChain and LlamaIndex, enabling RAG applications and LLM orchestration.
• Ability to meaningfully present results of analyses in a clear and impactful manner, breaking down complex ML/LLM concepts for non-technical audiences.
• Proven experience in leading and mentoring teams.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $203,300 and $305,600, 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.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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.

Apple accepts applications to this posting on an ongoing basis.

Key skills/competency

• Machine Learning
• Large Language Models (LLM)
• Generative AI (GenAI)
• Software Engineering
• Data Mining
• Distributed Computing
• Kubernetes
• LangChain
• LlamaIndex
• RAG applications