ML Applied Scientist, Apple Services Engineering Ai/ml

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

ML Applied Scientist role focused on designing, developing, and deploying AI/ML solutions for Apple's services, including LLMs and Agentic AI, to enhance user content discovery. The role involves research, prototyping, production deployment, A/B testing, and collaboration with cross-functional teams.

What you'd actually do

  1. Tackle complex research challenges by simplifying problems, inventing novel solutions, and driving concepts from ideation to production.
  2. Present key technical findings and research contributions to both internal teams and the wider public community.
  3. Design, fine-tune, and deploy Large Language Models (LLMs) and other advanced ML models for user-facing features.
  4. Develop and optimize high-performance components within large-scale distributed systems, using languages such as C++ and Go.
  5. Own the end-to-end deployment process, ensuring that features, code, data, and models are successfully launched and monitored in production.

Skills

Required

  • Python
  • PyTorch or TensorFlow
  • Generative AI
  • Agentic AI systems
  • Natural Language Processing (NLP)
  • Search Relevance and Ranking
  • Online Advertising
  • Recommendation Systems
  • Transformers
  • LLMs
  • model evaluation techniques
  • large-scale data pipelines

Nice to have

  • Master's or PhD
  • 4+ years of professional AI/ML experience
  • shipping production models
  • Search and Information Retrieval
  • indexing
  • query understanding
  • retrieval models
  • ranking algorithms
  • Retrieval-Augmented Generation (RAG)
  • agentic workflows
  • vector databases
  • knowledge graphs
  • Spark
  • Hadoop
  • Java
  • Scala
  • low-latency model serving systems
  • Go (Golang)
  • technical leadership
  • cross-functional projects
  • communication skills
  • collaboration

What the JD emphasized

  • shipping production models
  • end-to-end deployment
  • user-facing features
  • large-scale distributed systems
  • Generative AI
  • Agentic AI systems
  • NLP
  • Search Relevance and Ranking
  • Recommendation Systems
  • LLMs
  • model evaluation techniques
  • large-scale data pipelines
  • Retrieval-Augmented Generation (RAG)
  • agentic workflows
  • vector databases
  • knowledge graphs
  • large-scale data processing
  • pipeline construction
  • low-latency model serving systems

Other signals

  • shipping production models
  • large-scale distributed systems
  • user-facing features
  • end-to-end deployment