Machine Learning Engineer - Recommendations & Personalization (Feature Engineering)

Description

As a Machine Learning Engineer specializing in Recommendations & Personalization, you will be a pivotal contributor at the intersection of robust ML infrastructure, innovative recommendation systems, and emerging generative AI technologies. You will design, optimize, and deploy end-to-end recommendation flows - spanning sophisticated feature engineering, model training, real-time inference, and feedback loops. Simultaneously, you will prototype and build next-generation LLM-powered and agentic recommendation concepts that push the boundaries of what's possible. You will partner closely with applied researchers, infrastructure engineers, and data scientists to bring both production-grade ML systems and exploratory generative architectures to life. This is a hands-on, high-impact engineering role that bridges robust system design with forward-looking research and a passion for crafting unparalleled user experiences.","responsibilities":"Pioneer Generative Architectures: Collaborate with research teams to prototype, evaluate, and integrate LLM-driven or generative recommendation architectures, encompassing retrieval, sophisticated ranking, and conversational understanding.

Build Scalable ML Platform: Develop modular ML infrastructure and tooling that accelerates experimentation, safe deployment, and continuous integration-including robust model serving, versioning, rollback strategies, and online evaluation frameworks.

Craft High-Performance Services: Design, build, and maintain low-latency, high-throughput inference services in Go, Rust, Java, Python, or similar programming languages, operating at Apple's immense scale.

Optimize Recommendation Pipelines: Engineer, implement, and optimize large-scale recommendation and personalization pipelines, including both efficient batch processing and ultra-responsive real-time serving systems.

Advance Feature Engineering: Design and implement robust data and feature pipelines, including support for online feature stores, streaming updates, and real-time feature generation.

Enhance System Reliability: Partner with infrastructure teams to elevate system observability, reliability, and performance optimization across critical recommendation workloads.

Drive Model Evaluation: Lead the design and execution of A/B tests and continuous online evaluation of personalization models, ensuring alignment with product goals and measurable user impact.

Explore Agentic Systems: Participate in exploratory initiatives around agentic orchestration frameworks (e.g., LangGraph, LangChain) and their transformative application to adaptive recommendation workflows.

Preferred Qualifications

Strong theoretical understanding and hands-on experience in agent development, LLM fine-tuning, or post-training optimization.

Familiarity with or practical experience using modular LLM tooling frameworks such as LangGraph, LangChain.

Background in feature store design, embedding systems, or advanced vector retrieval techniques for recommendation pipelines.

Expertise in real-time inference, autoscaling strategies, traffic shaping, and cost-performance optimization for ML services.

Experience deploying and managing ML workloads on Kubernetes or other containerized environments.

Exposure to reinforcement learning, multi-objective ranking, or generative retrieval architectures.

Prior work experience in large consumer media or content recommendation domains.

Minimum Qualifications

BS, MS or PhD in Computer Science, Machine Learning, or a related technical field.

4+ years of hands-on experience developing and deploying production-grade ML systems for personalization, ranking, or recommendation.

Strong software engineering skills in Go, Rust, Java, Python, or similar languages, with a proven focus on building scalable, high-performance, and reliable services.

Extensive experience with distributed data and ML systems (e.g.,Ray, Spark) and model lifecycle management.

Deep understanding of recommendation model architectures, inference optimization techniques, and practical feedback loop implementations.

Demonstrated experience designing, implementing, and analyzing A/B tests or advanced online evaluation frameworks.

A strong commitment to system reliability, observability, and ultra-low latency in large-scale ML environments.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

Client-provided location(s): Seattle, WA

Job ID: apple-200634443-3337_rxr-658

Employment Type: OTHER

Posted: 2025-12-04T19:20:42