Senior ML Engineer - Ads ML Infrastructure

Apple•New York, NY

About The Position

The Senior ML Engineer will contribute to Apple’s Ad Platforms Machine Learning Platform team, which builds capabilities that help Ad Platforms teams develop and scale machine-learning features, models, and applications. The role involves designing and developing secure, scalable back-end systems that support advertising products, scalable modeling, and continuous experimentation. The engineer will work closely with engineers, data scientists, and other teams to build high-performing systems, shape architectures for ad network challenges, and select appropriate technologies. The work supports machine-learning products designed to deliver on Apple’s privacy commitments, with an emphasis on reliability, simplicity, scalability, and improving developer productivity.

Requirements

Proven experience designing and operating large-scale, low-latency ML serving platforms for real-time and batch inference.
Experience with model quantization, tensor parallelism, and inference optimizations such as ONNX Runtime, TensorRT, or vLLM.
Experience leading evaluation and adoption of machine-learning inference technologies.
Experience with distributed systems, such as Ray or high-throughput RPC systems, for scalable inference workloads and hybrid online/offline serving.
Hands-on experience designing and optimizing low-level GPU kernels.
Prior experience in the advertising industry, federated learning, and privacy-preserving machine-learning techniques.
Technical leadership and mentoring experience, including supporting engineers through code/design reviews, working groups, and internal knowledge sharing.
Experience leading development of foundational AI/ML platforms and tooling, including feature stores and vector databases.
Experience with performance tuning and troubleshooting.
Ability to build abstractions, automation tools, and reusable components to improve developer experience and streamline ML workflows.
Effective written and verbal communication with technical and non-technical multifunctional teams.
Results-oriented approach and ability to work in a fast-paced, collaborative environment.

Nice To Haves

PhD, MS, or BS in computer science or a related field.
Six or more years of experience in machine learning.
Strong software engineering skills.

Responsibilities

Design and develop secure, scalable back-end systems for machine-learning platform capabilities.
Design and operate large-scale, low-latency ML serving platforms supporting real-time and batch inference.
Evaluate and adopt technologies for model quantization, tensor parallelism, and inference optimization.
Build distributed systems for scalable inference workloads and hybrid online/offline serving patterns.
Design and optimize low-level GPU kernels to improve hardware utilization and accelerate deep-learning primitives.
Lead development of foundational AI/ML platforms and tooling, including feature stores and vector databases.
Tune performance and troubleshoot systems.
Build abstractions, automation tools, and reusable components to streamline ML workflows and reduce operational burden.
Contribute to architecture definition and refinement for ad network challenges.
Mentor engineers through code and design reviews, working groups, and knowledge sharing.
Collaborate and communicate with technical and non-technical multifunctional teams.