Machine Learning Engineer - Intelligent Quality Systems

Apple Apple · Big Tech · Cupertino, CA +1 · Machine Learning and AI

Machine Learning Engineer focused on applying ML, LLMs, computer vision, and retrieval systems to improve software quality and testing processes within Apple's OS development lifecycle. The role involves prototyping, evaluating, and building robust ML systems for tasks like test selection, failure triage, and visual validation, bridging research and production.

What you'd actually do

  1. Implement and refine ML solutions across the testing lifecycle, test selection, failure triage, visual validation, and test coverage analysis
  2. Rapidly prototype multiple algorithmic approaches to identify the most promising directions
  3. Design and implement data collection, labeling, and evaluation strategies for training and measuring ML systems
  4. Work with large-scale test result and code change data to surface patterns and inform modeling decisions
  5. Proactively identify research-to-production gaps and technical risks in proposed ML solutions
  6. Collaborate with applied scientists and software engineers to ship reliable, high-impact ML systems

Skills

Required

  • machine learning
  • ML fundamentals
  • model training
  • evaluation
  • debugging
  • programming
  • software engineering
  • production-quality code

Nice to have

  • NLP
  • large language models
  • code understanding
  • retrieval-augmented generation (RAG)
  • computer vision
  • multimodal ML
  • data pipelines
  • ML infrastructure
  • software testing
  • CI/CD systems
  • developer tooling
  • large-scale structured or semi-structured data
  • noisy, ambiguous, or expensive to obtain ground truth labels

What the JD emphasized

  • production-quality code
  • substantial ML projects beyond coursework
  • implement algorithms from papers or specifications
  • large-scale software systems
  • large-scale test result and code change data
  • large-scale structured or semi-structured data

Other signals

  • applying state-of-the-art ML, LLMs, computer vision, retrieval systems, and large-scale data analysis throughout the software lifecycle
  • transform software quality at scale
  • intelligently selecting which tests are most relevant to a code change
  • automatically triaging test failures to their root cause
  • validating UI and audio experiences with vision models
  • surfacing test coverage gaps from change descriptions and defect history
  • building the data platforms that tie it all together
  • prototype and evaluate techniques for problems like change-impact prediction, LLM-powered failure analysis, multimodal UI regression detection, and automated test recommendation
  • design data collection and evaluation strategies
  • work with large-scale test result and code change data
  • build systems robust enough to operate reliably across one of the world's largest software engineering organizations
  • crucial bridge between research ideas and production reality
  • identifying gaps in data quality, modeling assumptions, and system design before they become issues at scale
  • turning ideas into working ML systems
  • care deeply about measurement and rigorous evaluation
  • rewarding to see your work directly improve the productivity and quality of software shipped to hundreds of millions of people