Jobs and Careers

AIML - Full Stack ML Engineer, LLM Optimization

United Statesfull_timeVerifiedPosted 12 Jul 2024

💰 $300,200/yr($170,700/yr – $300,200/yr)

About the role

Summary

Posted: Jul 12, 2024
Weekly Hours: 40
Role Number:200554477

As a Machine Learning Engineer in the LLM Optimization team at Apple, you will have the opportunity to be part of an innovative ML organization that enables LLM for Apple products. The LLM Optimization team focuses on designing and implementing ML-based solutions to improve runtime latency, training time, memory usage, time to first token, and decoding speed across all Apple applications. The team is strategically positioned for significant contributions both in the short term (on well-known Apple products) and in the long term (on highly ambitious, high-risk, high-reward projects). This role emphasizes shipping ML-based features and products. As a Full Stack ML Engineer, you will innovate across the entire end-to-end ML production pipeline. Your responsibilities will include but are not limited to: * Designing new neural network architectures * Developing efficient model training and fine-tuning methods * Enhancing on-device and server side inference Our ideal team member is fearless in trying new things and willing to iterate on ideas. We value team members who can quickly prototype and iterate towards high-quality implementations.

Description

As a Full Stack ML Engineer on our team, you will leverage your background to: * Design and implement ML-based solutions to improve runtime latency, training time, memory usage, time to first token, and decoding speed for Apple applications * Innovate across the entire end-to-end ML production pipeline, including dataset creation, neural network architecture design, model training, fine-tuning methods, training time optimization, on-device and server side inference * Quickly prototype and iterate to achieve high-quality implementations for pioneering machine learning algorithms * Collaborate with hardware and software teams to integrate research findings into market-ready solutions * Translate theoretical ideas into tangible innovations, demonstrating their industrial applicability

Preferred Qualifications

Strong ML background
Proficiency in Programming Languages and Frameworks: Python, C++, PyTorch/TensorFlow/Jax
Experience with Natural Language Processing(NLP), ML optimization - with a focus on LLMs
Outstanding communication and technical writing skills, capable of conveying complex concepts clearly and efficiently
Preferred: notable achievements validated by quality publications in ML optimization, with a focus on LLMs

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 $170,700 and $300,200, 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 take affirmative action to ensure 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.