Job 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. 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 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

Requirements

Must-haves

This is not a DevOps-only or CI/CD-focused role. We are looking for engineers who build platform solutions, not just configure pipelines.
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
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.
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

Nice-to-haves

AWS certifications or broad experience across multiple AWS services is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlci/cdawspythonjavarustgokubernetes

Platform: apple
Effort: medium