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# Senior/Staff Software Engineer - AI, Search & Knowledge Platforms

- **Company:** Apple Inc.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $171,600.0 - $302,200.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Serialization, Data Stores, Protocol Buffers, Job Scheduling, Python (Programming Language), PostgreSQL, Machine Learning, Object-Oriented Software Development, Redis, Software Deployment, Workflow Management Systems, Private Cloud Environment, Data Processing, Kotlin, Containerization, Kubernetes, Information Technology, Functional Programming, Software Version Control, Docker
- **Published:** May 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e8ff9660db7349d3

## About the Role

Do you have experience in Cross-functional communication?, Do you have a Bachelor's degree?, Experience with gRPC, Protobuf, or similar serialization/RPC frameworks.

Experience building CI/CD pipelines

Experience with workflow orchestration frameworks such as Temporal or job scheduling systems

Experience with Python for scripting, automation, or data processing

Experience with PostgreSQL, Redis, or similar data stores in high-throughput environments

Minimum Qualifications

Proficient in Kotlin or Java, with strong object-oriented and functional programming skills

5+ years of experience

Experience with containerized environments (Kubernetes, Docker)

Demonstrated experience and ability to collaborate across large, cross-functional teams

Strong analytical and problem-solving skills with the ability to reason about complex compatibility and dependency constraints

B.S. in Computer Science or equivalent

## Description

Design, develop, and maintain services and orchestration workflows that validate, qualify, package, and deploy ML models to end-user devices and Private Cloud Compute servers.

Build and evolve the compatibility and versioning systems that target the right models to the right devices, accounting for hardware capabilities, OS versions, feature sets, locales, and inter-model dependencies.

Collaborate with ML, OS, and platform teams to improve the end-to-end model release process from qualifying models to production deployment.


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