Full Stack Software Engineer

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!

Responsibilities:

Build and operate demand and capacity planning systems

Build data pipelines and telemetry systems that ingest, normalize, and serve fleet-wide utilization and cost data across multi-tenant and heterogeneous fleets

Develop observability infrastructure - monitoring, alerting, and dashboards - that surfaces real-time fleet health and efficiency signals

Drive innovation in forecasting, optimization, and supply chain management tooling that works at scale

Build end-to-end tooling - from data models and APIs to interactive dashboards - that distills complex data into actionable insights for leadership

Build self-service platforms with well-defined schema contracts and APIs, enabling ML teams, infrastructure engineers, and finance to balance usability, utilization, and costs

Engage cross-functionally with finance analysts, supply chain managers, data center operations, compute infrastructure engineers, and more

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

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

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

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.