Sr. Machine Learning Engineer – Recommendations & Personalization (feature Engineering)

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

This role focuses on operationalizing machine learning models for recommendations and personalization systems at Apple. The primary responsibility is building and managing real-time and batch inference pipelines, optimizing system performance, and driving experimentation. The role bridges research and production by developing infrastructure, tooling, and monitoring for shipping ML-driven features. It involves partnering with ML researchers, designing inference services, building data pipelines, developing deployment and evaluation tooling, leading A/B testing, and collaborating on observability and system reliability. While the core is inference (L3), the mention of agents and LLMs in preferred qualifications suggests a secondary focus on agentic systems (L4).

What you'd actually do

  1. Partner with ML researchers and product teams to transition models into production, ensuring reliability, scalability, and low latency.
  2. Design and implement robust inference services using object-oriented languages (e.g., Java, Scala, C++) that operate at scale across Apple platforms.
  3. Build and manage data pipelines and model execution frameworks to support both batch and streaming use cases.
  4. Develop tooling and infrastructure for model deployment, versioning, rollback, and online evaluation.
  5. Lead A/B testing efforts, including integration, metric tracking, experiment validation, and performance analysis.

Skills

Required

  • MS or PhD in Computer Science, Software Engineering, or related field
  • 8+ years of deep software engineering experience
  • building and deploying production machine learning systems
  • big data and stream processing frameworks like Spark, Flink, or Kafka
  • Proficiency in object-oriented programming languages such as Java, Scala, or C++
  • building and maintaining large-scale distributed systems for ML workloads
  • Deep understanding of ML model deployment pipelines, runtime optimization, and system integration
  • Familiarity with A/B testing frameworks, experimental design, and online evaluation
  • Strong focus on system reliability, latency, and observability in production environments

Nice to have

  • Experience in personalization, search, or recommendations
  • Experience in batch and real-time inference serving, including autoscaling and traffic management
  • Background in content recommendation systems, search ranking, or user engagement optimization
  • Experience with CI/CD workflows for ML systems, including safe model rollouts and shadow testing
  • Exposure to containerized deployments and orchestration (Kubernetes, Docker)
  • Experience building and deploying production-grade applications using LLMs, including expertise in prompt engineering, RAG pipelines, and framework orchestration
  • Proven track record of developing autonomous agents capable of multi-step reasoning, external tool integration, and complex task decomposition to solve open-ended problems
  • Prior experience working on consumer-scale media products (apps, games, books, music, or video)

What the JD emphasized

  • 8+ years of deep software engineering experience
  • building and deploying production machine learning systems
  • large-scale distributed systems for ML workloads
  • ML model deployment pipelines
  • system reliability, latency, and observability in production environments

Other signals

  • operationalizing machine learning models
  • building real-time and batch inference pipelines
  • optimizing system performance, reliability, and experimentation velocity
  • shipping ML-driven features safely and efficiently
  • scaling ML solutions
  • building production-grade services
  • driving experimentation across billions of users