DevOps/Backend Software Engineer

Summary

Are you passionate about first-class infrastructure and software development, and eager to apply your expertise to solve real-world problems at Apple’s incredible scale? Do you want to see your work have direct impact on web services, applications, and deployments used by thousands of engineers every day?Come join us in developing, shipping, and maintaining AI/ML and Generative AI services — including the infrastructure, evaluation pipelines, and integration workflows that provide cutting-edge features to support Apple’s hardware product development teams, helping bring amazing, groundbreaking products and innovations to life.

We are the Product Integrity AI/ML team, and we build and deliver software supporting the development of Apple’s unparalleled hardware product line. Our software is used by both Apple engineers and third-party accessory manufacturers to develop and debug their systems, with high visibility throughout the company.

Description

As a DevOps/Backend Engineer in our team, you will design, implement, and manage the infrastructure that powers all of our services, tools, and apps. You will work with a diverse array of cross-functional partners throughout Apple on challenging projects incorporating Machine Learning, Generative AI, and MLOps — collaborating closely with ML engineering teams to integrate, evaluate, and operationalize models within our services. The services and software you craft will be instrumental in solving difficult challenges, providing data insights, and driving decision-making within Hardware Engineering and beyond. We move at a fast pace, iterate quickly, and work side-by-side with our customers to ensure we’re building the most effective solutions possible.

Responsibilities

• Design, build, maintain, and manage cloud infrastructure platforms using IaC to service cutting edge machine learning workflows
• Collaborate with ML engineering partners to integrate and operationalize hosted models into our services, building reliable pipelines for model evaluation, A/B testing, and performance benchmarking
• Develop and maintain infrastructure for GenAI-powered evaluation and testing workflows, ensuring our internal tooling can assess and compare models across different parts of our systems
• Write high-quality code that’s testable, scalable, and able to be maintained by others
• Work closely with software developers in our team providing infrastructure expertise, cloud integration best-practices, and service architecture guidance
• Collaborate across teams and organizations to distill complex requirements into a concrete action plan
• Lead design reviews, author documentation, and give meaningful feedback on the designs of peers
• Represent your work to the team and leadership through demos, presentations, and retrospectives

Minimum Qualifications

• 3+ years experience in SRE/DevOps, systems engineering, build/release/deployment, and/or automation
• Proficient in implementing applications in private/public cloud infrastructure and container technologies, including but not limited to: Kubernetes, Docker, database platforms, and event/data pipelines, and model-serving or API integration patterns
• Experience designing, building and managing CI/CD pipelines
• Experience with networking load balancers such as HAProxy, NGINX, etc.
• Demonstrated ability to write applications in a high-level programming language like Python, Ruby, Java, etc.
• Excellent written and verbal communication skills to both technical and non-technical audiences
• Bachelor’s Degree in Computer Science, Computer Engineering, related field, or equivalent work experience

Preferred Qualifications

• Experience building scalable, maintainable, robust web-services and applications
• Ability to architect complex systems in a reusable, modular way
• Experience building infrastructure to support Generative AI services, including model integration, evaluation pipelines, and A/B testing frameworks
• Familiarity with MLOps practices such as model versioning, pipeline orchestration, experiment tracking, and performance monitoring in production environments
• Curiosity to learn new technologies and passion for sharing that knowledge with others
• Master’s degree in Computer Science, Computer Engineering, related field, or equivalent work experience