Machine Learning Engineer, Proactive

Description

Minimum Qualifications

• Bachelor degree in Computer Science, Machine Learning,
• Artificial Intelligence, or a related field.
• Background in machine learning, deep learning, natural
• language processing, information retrieval, search,
• recommender systems, or generative AI.
• Experience training, fine-tuning, or deploying transformer-
• based models and large language models.
• Experience with modern deep learning architectures and
• techniques, including transformers, embeddings,
• representation learning, and neural ranking.
• Programming skills in Python and/or C/C++, with experience
• building production-quality software using modern machine
• learning frameworks such as PyTorch, JAX, or TensorFlow.
• Ability to work onsite in Cupertino, California, in accordance
• with Apple's applicable work policies.

Preferred Qualifications

• Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Experience optimizing machine learning models for resource-constrained environments, including knowledge distillation, model compression, quantization, and pruning.
• Experience with on-device machine learning or edge AI, or mobile inference frameworks, including optimizing models for latency, memory, compute, and power constraints.
• Experience distilling capabilities from large foundation models into small language models or task-specific models for efficient inference.
• Experience building retrieval-augmented generation, vector search, embedding retrieval, neural reranking, or semantic search systems.
• Experience with query understanding, query rewriting, intent classification, personalized retrieval, learning-to-rank, or recommendation models.
• Experience working with transformer architectures and foundation model families such as BERT, T5, Llama, Gemma, Mistral, or related architectures.
• Experience evaluating language models, designing AI quality metrics, and building automated and human-in-the-loop evaluation pipelines.
• Experience building large-scale production search, recommendation, personalization, or generative AI systems.
• Familiarity with multimodal foundation models, tool use, agentic AI, or agentic retrieval systems.
• Strong understanding of the tradeoffs among model quality, latency, memory, power consumption, privacy, and reliability for production on-device AI systems.
• Ability to prototype new ideas, conduct rigorous experiments, solve ambiguous technical problems, and translate research advances into production-quality machine learning solutions.