Machine Learning Engineer, Ase Search Team

Apple Apple · Big Tech · Seattle, WA +2 · Machine Learning and AI

Machine Learning Engineer on the Video Search team at Apple, focusing on building and deploying large-scale ML systems for search and discovery across Apple platforms. The role involves applying ML, NLP, and generative AI to model user intent, optimize retrieval and ranking systems, and enhance search relevance and personalization for millions of users.

What you'd actually do

  1. Solve complex research problems and implement solutions from concept to execution.
  2. Design and implement retrieval and ranking systems using semantics and user context.
  3. Build and deploy ML, NLP and LLM models to improve search relevance and personalization.
  4. Analyze data and model performance to identify opportunities for search quality enhancement.
  5. Develop automated tests for continuous integration and ensure successful production deployment.

Skills

Required

  • Experience in machine learning, NLP, IR, or more recently Large Language Model ( LLMs).
  • Strong programming skills in Python, Java and Go for building scalable ML systems.
  • Hands-on expertise in ML libraries such as PyTorch, JAX, TensorFlow for model training and deployment.
  • Ability to translate product goals into technical solutions, improving user experience.
  • Strong communication, collaboration, and analytical problem-solving skills.
  • In-depth knowledge of search and information retrieval fundamentals, including indexing and ranking.
  • Experience with retrieval and ranking algorithms and building big data pipelines using Hadoop, Java, Scala, Spark and more.
  • Industrial experience in search, classification, recommendation systems, or related fields.
  • Familiarity with A/B testing and data-driven product development.
  • Bachelor’s degree or higher (or equivalent practical experience) in Computer Science, Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field.

Nice to have

  • Experience with search or recommendation systems, and semantic retrieval or vector databases.
  • Expertise in transformer architectures, embeddings, and retrieval or ranking models.
  • Experience in applying or fine-tuning LLMs for understanding and generation tasks.
  • Familiarity with prompt design, context management, RAG and Agentic architectures and solutions.
  • Exposure to evaluation and safety frameworks for LLM-based systems.
  • Knowledge of reinforcement learning and other modern post training practices for LLMs.

What the JD emphasized

  • large-scale ML systems
  • global scale
  • strict privacy standards
  • advanced ML technologies
  • search relevance
  • personalization
  • user intent
  • retrieval pipelines
  • ranking systems
  • LLM models
  • search quality enhancement
  • global Apple devices
  • all platforms and languages
  • content discovery features
  • privacy, efficiency, and user experience standards
  • semantic retrieval
  • vector databases
  • transformer architectures
  • embeddings
  • retrieval or ranking models
  • applying or fine-tuning LLMs
  • prompt design
  • context management
  • RAG
  • Agentic architectures and solutions
  • evaluation and safety frameworks for LLM-based systems
  • reinforcement learning
  • modern post training practices for LLMs

Other signals

  • develop scalable systems and machine learning models that drive search relevance, personalization, and understanding of content at scale
  • apply machine learning, natural language understanding, and generative AI to model user intent and deliver relevant, personalized results
  • building and optimizing cutting edge data processing, ML models, retrieval pipelines, and ranking systems that operate at global scale and under strict privacy standards