About the role

What will you do at Apple?

Scaling machine learning workloads across thousands of accelerators creates

challenges that few engineers ever encounter. In Apple’s Machine Learning

Platform Technologies organization, we build the infrastructure that powers

large-scale ML training and inference workloads, bringing together expertise in

distributed systems, machine learning infrastructure, and high-performance

computing.

DESCRIPTION

As a senior engineer on the ML Compute Capacity team, you will design, build,

and operate the production systems that ensure compute resources are optimally

distributed throughout the company. You'll work across the stack — from data

pipelines and backend services to APIs and interactive frontends — developing

telemetry systems, optimization algorithms, policies, and intuitive tools for

managing demand and improving efficiency across Apple's largest accelerator

fleet. Our small, nimble team works in a high-autonomy, fast-paced environment,

and we're passionate about digging into data patterns, laying out the

performance characteristics of an entire distributed system, and knowledge

sharing. If the opportunity to own and operate services that scale, stay highly

available, and "just work" excites you, then please reach out to us!

MINIMUM QUALIFICATIONS

5+ years of experience in relevant areas Proficiency in Python for production

backend and data engineering work Experience building data pipelines and

crafting robust queries over large-scale, multi-source data (e.g., Trino,

PostgreSQL, Elasticsearch) Experience designing and building RESTful APIs and

working with cloud storage technologies Experience with modern web frameworks

like React Experience with observability tools (e.g., Prometheus, Grafana) or

equivalent monitoring systems Excellent problem-framing and problem-solving

skills Strong CS fundamentals Bachelor's degree or higher in Engineering,

Mathematics, Economics, or a related quantitative field

PREFERRED QUALIFICATIONS

Experience operating Kubernetes at production scale — including scheduling,

resource management, and cluster debugging Familiarity with accelerator

utilization patterns across ML training and inference Strong interest with

capacity planning, cost attribution, or FinOps systems

Which skills does this role require?

PythonDistributed systemsMachine learning infrastructureData pipelinesRESTful APIsReactPrometheusGrafanaTrinoPostgreSQLElasticsearchTelemetry systemsHigh-performance computingMachine learningTelemetryCloud storageInfrastructureOptimization algorithmsAccelerator fleetREST APIsMachine Learning

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