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Apple Machine Learning Engineer – Recommendations & United States, Washington, Seattle 994536549
Yesterday
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In this role, you will be responsible for operationalizing machine learning models—from building real-time and batch inference pipelines to optimizing system performance, reliability, and experimentation velocity. You’ll help bridge the gap between research and production by developing the infrastructure, tooling, and monitoring required to ship ML-driven features safely and efficiently. Key Responsibilities* Partner with ML researchers and product teams to transition models into production, ensuring reliability, scalability, and low latency.* Design and implement robust inference services using object-oriented languages (e.g., Java, Scala, C++) that operate at scale across Apple platforms.* Build and manage data pipelines and model execution frameworks to support both batch and streaming use cases.* Develop tooling and infrastructure for model deployment, versioning, rollback, and online evaluation.* Lead A/B testing efforts, including integration, metric tracking, experiment validation, and performance analysis.* Collaborate with infrastructure teams to improve observability, alerting, and model health monitoring.* Drive continuous improvement in latency, throughput, fault tolerance, and overall system reliability.
- MS or PhD in Computer Science, Software Engineering, or related field—or equivalent industry experience.
- 2+ years of experience in production machine learning systems, especially for personalization or recommendations.
- Proficiency in object-oriented programming languages such as Java, Scala, or C++.
- Experience 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.
- Experience with big data and stream processing frameworks like Spark, Flink, or Kafka.
- Strong focus on system reliability, latency, and observability in production environments.
- 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).
- Prior experience working on consumer-scale media products (apps, games, books, music, or video).
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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