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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 Apple Ads teams. As a key contributor to this team, you will design and develop model training and fine-tuning infrastructure at scale. You will enjoy building high-performing, elegant machine learning systems from the ground up, in close partnerships with various teams, both within and outside Apple Ads. You will also possess keen judgment in selecting technologies and building the right solution for the interesting challenges we get to tackle here. You will have the opportunity to define and refine architectures to meet the unique ad network challenges we must solve. 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 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 (Transformers, LLMs, DNNs) and training frameworks (TensorFlow, 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 & distillation. Prior experience building AI/ML tooling for model fine-tuning and training, and/or infrastructure at scale Ability to communicate effectively, both written and verbal, 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.
Preferred Qualifications
Prior experience in privacy-preserving ML using techniques such as federated learning and differential privacy Experience with LLM training and inference — pre-training, SFT, verifiable RL rewards, inference-Familiarity with Agentic AI PhD/MS/BS in Computer Science or related field with 10+ years of industry experience in building ML systems
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