The Machine Learning Platform team at Apple Ads is focused on empowering teams to develop and operate innovative machine learning applications at scale. This role involves collaborating with ML engineers and scientists to enhance platform capabilities that improve ML features and models. The engineer will design and develop infrastructure for model training and fine-tuning while ensuring the solutions meet unique advertising challenges. The position emphasizes building high-performing machine learning systems and contributing to a culture of reliability and scalability.

• 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 such as Transformers, LLMs, and DNNs, along with training frameworks like TensorFlow and 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, and distillation.
• Prior experience building AI/ML tooling for model fine-tuning and training, and/or infrastructure at scale.
• Ability to communicate effectively 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.
• Prior experience in privacy-preserving ML using techniques such as federated learning and differential privacy.
• Experience with LLM training and inference, including pre-training, SFT, verifiable RL rewards, and familiarity with Agentic AI.
• PhD/MS/BS in Computer Science or related field with 10+ years of industry experience in building ML systems.

• Design and develop model training and fine-tuning infrastructure at scale.
• Build high-performing machine learning systems in collaboration with various teams.
• Select appropriate technologies and build solutions for complex challenges.
• Define and refine architectures to address unique ad network challenges.
• Contribute to building machine learning products that uphold Apple's privacy commitments.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.