Software Reliability Engineer, GenAI & ML Platform

Apple, Inc.

Sunnyvale (CA)

On-site

USD 150,000 - 225,000

Full time

11 hours ago
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Benefits offered by this job

Comprehensive medical and dental cover
Retirement benefits
Discounted products and free services
Tuition reimbursement
Relocation assistance

Job summary

Apple, Inc. is seeking a Software Engineer in Reliability Engineering for the AiDP org in Sunnyvale, CA.

You will join the Applied Machine Learning team to design, operate, and scale large distributed systems that support ML, GenAI, inference, and data platforms across Apple's enterprise ecosystem. The role emphasizes building multi-tenant services, collaborating with cross-functional teams, and guiding projects from design through release while maintaining uptime and performance in a hybrid

Qualifications

  • BS/MS in computer science or equivalent experience.
  • 2+ years programming in Python, Java, or Go.
  • 2+ years experience with Kubernetes, Docker, or other container orchestration framework.

Responsibilities

  • Build, enhance, and maintain multi-tenant systems leveraging diverse technologies.
  • Collaborate with cross-functional teams to deliver impactful customer features.
  • Lead projects through full lifecycle, from design discussions to release delivery.
  • Operate, scale, and optimize high-throughput and highly concurrent services.
  • Diagnose, resolve, and prevent production and operational challenges.

Skills

Python
Java
Go

Education

BS/MS in Computer Science

Tools

Kubernetes
Docker

Job description

Apple, Inc. is seeking a Software Engineer in Reliability Engineering for the AiDP org in Sunnyvale, CA.

You will join the Applied Machine Learning team to design, operate, and scale large distributed systems that support ML, GenAI, inference, and data platforms across Apple's enterprise ecosystem. The role emphasizes building multi-tenant services, collaborating with cross-functional teams, and guiding projects from design through release while maintaining uptime and performance in a hybrid

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