Job 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! 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 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

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

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Keywords to mirror

mlmachine learningci/cdllmai

Platform: apple
Effort: medium