Sr Machine Learning Engineer ML Data

At Apple we work every day to create products that enrich peoples 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 Apple Ads Machine Learning Platform teams 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.

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 Apples privacy commitments and change the way advertising works with 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 others growth and supporting each other in delivering for our customers!

Experience writing mission-critical code for production machine learning systemsnExperience building ML infrastructure frameworks or services used by multiple teamsnExperience building or operating feature stores or comparable ML data infrastructure serving production modelsnExperience with embedding management: generating and versioning embeddings refresh and retirement policy and storing and serving them for retrieval at scalenSolid understanding of the ML lifecycle: training evaluation deployment and serving/inferencing with working experience building and deploying modelsnUnderstanding of model evaluation train-serve skew and data driftnWorking knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlownPrior experience applying ML at scale in advertising recommender systems information retrieval or related domainsnExperience with training data generation across multi-modal data (text image and structured) including sampling and point-in-time correctnessnExperience building production data pipelines for ML systems where scale and performance are critical using distributed processing systems. nExperience building ML systems using batch and streaming deployments workflow orchestration and modern storage formats nStrong data modeling and data architecture skills with a high bar for system and data quality: correctness reliability testing and validationnStrong problem solving debugging and performance tuning skills and pride in building automation tooling and CI/CDnResults oriented with the ability to communicate effectively both written and verbal with technical and non-technical multi-functional teamsnProduct-minded with a proven ability to seek projects with a sense of ownership

Preferred QualificationsnnExperience in advertising industry nExperience with LLM-based data generation or evaluationnFamiliarity with large-scale distributed training and its demands on data infrastructurenPrior experience in privacy-preserving MLnFamiliarity with agentic AInnnnEducation u0026 ExperiencenPhD 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