Senior Staff Machine Learning Engineer, ML Platform

Apple, Inc.; New York (NY); On-site; USD 263,000 - 394,000; Full time

Benefits offered by this job

Stock programs

Employee stock purchase plan

Education reimbursement

Job summary

Apple Inc. in New York City is seeking a Senior Staff Machine Learning Engineer for the ML Platform.

You will design and build platform capabilities to enable Apple Ads teams to train, fine-tune, and deploy ML models at scale while upholding Apple’s privacy commitments. You will collaborate with ML engineers and data scientists to create reliable training pipelines and inference services, selecting architectures and tooling to scale across teams.

Qualifications

• Experience building shared ML platforms used by multiple teams.
• Deep understanding of the ML lifecycle, including training pipelines, evaluation methodologies, and deployment patterns.
• Deep understanding of deep learning architectures (Transformers, LLMs, DNNs) and training frameworks (TensorFlow, PyTorch).
• Prior experience applying ML at scale in Ads, recommender systems, information retrieval or related domains.
• Prior experience in distributed training at scale and optimization techniques like pruning, compression, quantization & distillation.
• Ability to communicate effectively with technical and non-technical teams.
• Results oriented with strong technical leadership and ownership mindset.
• Curious business attitude with drive to own projects.

Responsibilities

• Design and develop model training and fine-tuning infrastructure at scale.
• Build and operate high-performing ML platform capabilities across teams.
• Collaborate with ML engineers and scientists to improve features, models, and applications.
• Define architectures that meet unique advertising network challenges and privacy commitments.

Skills

ML platforms

ML lifecycle

Transformers

TensorFlow

PyTorch

Distributed training

Communication

Leadership

Ads domain

Education

PhD in Computer Science

MS in Computer Science

BS in Computer Science

Tools

TensorFlow

PyTorch

Airflow

Kubernetes

Job description

Senior Staff Machine Learning Engineer, ML Platform

New York City, New York, United States Software and Services

At Apple, we work every day to create products that enrich people’s lives. Apple Ads makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Today, our technology and services power advertising in Search Ads, App Store, and Apple News. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy.The Machine Learning Platform team’s mission is to empower teams at Apple Ads to easily and rapidly develop, deploy and operate innovative ML applications at scale by providing a self-serve, unified platform with foundational infrastructure in model training, inference and agentic AI, as well as associated data and application services.Are you a results-oriented and versatile engineer who can excel in an Agile environment? You will work closely with other ML engineers and scientists to design, develop, and build world-class platform capabilities that will enable Apple Ads teams to improve and scale our ML features, models, and applications.

Description

The ML Platform team is responsible for bringing numerous features to advertisers and consumers while simultaneously supporting scalable modeling and continuous experimentation by all Apple Ads teams. As a key contributor to this team, you will design and develop model training and fine-tuning infrastructure at scale. You will enjoy building high-performing, elegant machine learning systems from the ground up, in close partnerships with various teams, both within and outside Apple Ads. You will also possess keen judgment in selecting technologies and building the right solution for the interesting challenges we get to tackle here. You will have the opportunity to define and refine architectures to meet the unique ad network challenges we must solve. You will play a meaningful role building machine learning products which deliver on Apple's privacy commitments and change the way advertising works with data.Join us and contribute to a culture that emphasizes reliability, simplicity, and scalability. You will join a team of world-class machine learning engineers hungry to apply leading-edge technologies to deliver extraordinary experiences to our customers. We are one team, nurturing each other’s growth and supporting each other in delivering for our customers!

Minimum Qualifications

• Experience building shared ML platforms, frameworks or services used by multiple teams or organizations.
• Deep understanding of the ML lifecycle, including training pipelines, evaluation methodologies, and deployment patterns.
• Deep understanding of deep learning architectures (Transformers, LLMs, DNNs) and training frameworks (TensorFlow, PyTorch)
• Prior experience applying ML at scale in Ads, recommender systems, information retrieval or related domains.
• Prior experience in distributed training at scale and optimization techniques like model pruning, compression, quantization & distillation.
• Prior experience building AI/ML tooling for model fine-tuning and training, and/or infrastructure at scale
• Ability to communicate effectively, both written and verbal, with technical and non-technical multi-functional teams
• Results oriented with strong technical leadership skills and a desire to work in a fast-paced collaborative work environment
• Curious business attitude with a proven ability to seek projects with a sense of ownership.

Preferred Qualifications

• Prior experience in privacy-preserving ML using techniques such as federated learning and differential privacy
• Experience with LLM training and inference — pre-training, SFT, verifiable RL rewards, inference-Familiarity with Agentic AI
• PhD/MS/BS in Computer Science or related field with 10+ years of industry experience in building ML systems

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 $262,500 and $394,000, 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

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.