Site Reliability Engineer - ML, Apple Ads

At Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes—from small app developers to big, global brands. Because when advertising is done right, it benefits everyone. The Site Reliability Engineering team within Apple Ads ensures the reliability, performance, and availability of ML Platform and Services at scale. The team partners closely with Ads engineering, data science and ML platform teams to enable product delivery through design, configuration, and automation of machine learning infrastructure powering Apple Ads applications. We are looking for a ML Platform Infrastructure Engineer to help build and evolve the next generation of Apple Ads machine learning platform — enabling fast, reliable, and scalable operations across AWS-based environments supporting transactional and analytical workloads.

Description

As a site reliability engineer in Apple Ads focused on machine learning, you will own the health, performance, and scalability of large scale infrastructure powering ML training, inference, serving workloads and associated platform tooling. Your focus will be on building automation that eliminates manual processes, improves platform resilience, and enables teams to move faster with confidence. This is not a DevOps-only or CI/CD-focused role. We are looking for engineers who build platform solutions, not just configure pipelines.

Minimum Qualifications

3+ years of experience in internet-facing backend production systems, SRE or ML Operations focused roles on large scale distributed cloud infrastructure Proven expertise with AWS-managed infrastructure Familiarity with ML lifecycle and associated technologies such as NVIDIA Triton, AnyScale Ray, Apache Airflow etc. Strong programming skills in at least one of: Python, Java, Rust, Go or similar languages Hands-on experience with Linux systems and deep knowledge of its internals. Demonstrated experience with Infrastructure as Code, especially Terraform. Strong foundation in SRE concepts: Monitoring, alerting, observability, Incident response and root cause analysis, Error budgets, SLAs/SLOs, and system reliability

Preferred Qualifications

Built tools or services that automate platform operations, reduce toil, or improve cost efficiency. Experience managing Kubernetes clusters at scale in production environments. Hands-on experience troubleshooting distributed systems under real-world load. Clear communication skills and comfort collaborating across engineering, infrastructure, and product teams. AWS certifications or broad experience across multiple AWS services is a plus. Understanding of modern GPU hardware architectures (such as NVIDIA H100, B200, or GB200, AWS Inferentia ), associated drivers Understanding of high-performance fabrics and network architecture, power, and thermal limits