Sr Machine Learning Engineer - ML Data

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!

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

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 Education & Experience 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