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# Software Development Engineer, AI Platform

- **Company:** Adobe Systems
- **Location:** Seattle, WA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Adobe Acrobat, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Unit Testing, Microsoft Azure, Cloud Computing, Code Review, Collaborative Learning, Software Debugging, Software Design Patterns, Python (Programming Language), Modular Design, Node.Js, Object-Oriented Software Development, Software Engineering, Model Validation, Backend, Adobe, Containerization, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Docker
- **Published:** August 8, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/software-development-engineer-ai-platform-seattle-wa-usa-58856652

## About the Role

Development asynchronous system design, and testable codebases * Participate in code reviews and uphold engineering quality and collaborative learning * Contribute to service releases and support globally deployed systems with operational rigor * Automate ML workflow steps, including evaluation harnesses and prompt pipeline testing * Collaborate with ML developers and feature teams to translate requirements into engineering solutions Tasks * B.S. or M.S. in Computer Science or equivalent experience * >2 years of production software engineering experience, backend services and infrastructure * Proficiency in Python with clean, tested, documented code; familiarity with Pydantic or LangChain is a plus * Experience with concurrent and asynchronous systems in Python, Node.js, or Go * Solid understanding of OOP and common design patterns * Experience with event-driven, non-blocking I/O architectures * Proficiency in unit and integration testing and debugging across service boundaries * aaaaaa
teams with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) * Strong communication and collaboration across engineering and ML teams Key requirements * comprehensive benefits programs

## Description

Experteer Overview As a Software Development Engineer on Adobe Document Cloud's AI team, you will design and maintain scalable backend services and ML pipelines powering Acrobat AI Assistant. You'll build tooling, APIs, and interfaces that empower ML engineers to move from prototype to production. You'll collaborate with ML teams to deliver robust, observable systems deployed across cloud, desktop, and mobile at global scale. This role offers a hands-on path at the intersection of software engineering and applied AI, with a clear impact on millions of PDFs and billions of transactions. Compensation / Benefits * Design, build, and maintain scalable backend services and APIs for Acrobat AI Assistant features and ML pipelines * Develop and maintain data pipelines for model evaluation, prompt testing, and feature monitoring with reliability and modular design * Create internal tooling, SDKs, and abstractions to reduce toil for ML Engineers and speed production delivery * Enforce clean architecture, asynchronous system design, and testable codebases * Participate in code reviews and uphold engineering quality and collaborative learning * Contribute to service releases and support globally deployed systems with operational rigor * Automate ML workflow steps, including evaluation harnesses and prompt pipeline testing * Collaborate with ML developers and feature teams to translate requirements into engineering solutions Tasks * B.S. or M.S. in Computer Science or equivalent experience * >2 years of production software engineering experience, backend services and infrastructure * Proficiency in Python with clean, tested, documented code; familiarity with Pydantic or LangChain is a plus * Experience with concurrent and asynchronous systems in Python, Node.js, or Go * Solid understanding of OOP and common design patterns * Experience with event-driven, non-blocking I/O architectures * Proficiency in unit and integration testing and debugging across service boundaries * Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes) * Strong communication and collaboration across engineering and ML teams Key requirements * comprehensive benefits programs


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