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Apple

Sr Machine Learning Engineer - ML Data

Cupertino, CA$184,700 – $277,600Posted 29 days ago

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At Apple, we work every day to create products that enrich people's lives. Our Apple Ads group 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 the App Store, Apple Maps, and Apple News. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy.

The Apple Ads Machine Learning Platform team's mission is to empower Ads teams to build and scale the innovative ML systems that deliver highly optimized advertising content to consumers. Are you a results-oriented and versatile engineer who can excel in a fast-paced environment? You will work closely with ML engineers and scientists to design, develop, and build world-class platform capabilities that will enable 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 Ads teams. As a key contributor to this team, you will design and develop secure and scalable back-end systems. You will enjoy building high-performing, elegant systems from the ground up, in close partnerships with various teams. You will also possess keen judgment in selecting technologies and building the right solutions for the unique ad network challenges we face. 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!

Preferred Qualifications

Preferred Qualifications

Experience in advertising industry

Experience with LLM-based data generation or evaluation

Familiarity with large-scale distributed training and its demands on data infrastructure

Prior experience in privacy-preserving ML

Familiarity with agentic AI

PhD in Computer Science or related field with 3+ years of engineering experience and 5+ years of machine learning experience; or MS in Computer Science or related field with 6+ years of engineering experience and 5+ years of machine learning experience; or BS in Computer Science or related field with 7+ years of engineering experience and 5+ years of machine learning experience

Minimum Qualifications

Experience writing mission-critical code for production machine learning systems

Experience building ML infrastructure, frameworks or services used by multiple teams

Experience building or operating feature stores, or comparable ML data infrastructure serving production models

Experience with embedding management: generating and versioning embeddings, refresh and retirement policy, and storing and serving them for retrieval at scale

Solid understanding of the ML lifecycle: training, evaluation, deployment and serving/inferencing, with working experience building and deploying models

Understanding of model evaluation, train-serve skew and data drift

Working knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlow

Prior experience applying ML at scale in advertising, recommender systems, information retrieval or related domains

Experience with training data generation across multi-modal data (text, image and structured), including sampling and point-in-time correctness

Experience building production data pipelines for ML systems where scale and performance are critical using distributed processing systems.

Experience building ML systems using batch and streaming deployments, workflow orchestration and modern storage formats

Strong data modeling and data architecture skills, with a high bar for system and data quality: correctness, reliability, testing and validation

Strong problem solving, debugging and performance tuning skills, and pride in building automation, tooling and CI/CD

Results oriented, with the ability to communicate effectively, both written and verbal, with technical and non-technical multi-functional teams

Product-minded with a proven ability to seek projects with a sense of ownership

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 $184,700 and $277,600, 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.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology

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