Software Development Engineer, AI Platform

About the role

Adobe Document Cloud's AI team is building the next generation of AI-powered features for Acrobat AI Assistant, which processes billions of PDFs and millions of transactions monthly. We're seeking a Software Development Engineer to build and maintain the backend services, tooling, and pipelines that enable our Machine Learning Engineers to develop, evaluate, and deploy production-ready AI features efficiently.

Responsibilities

• Design, build, and maintain scalable backend services and APIs supporting Acrobat AI Assistant features and ML pipelines.
• Develop and maintain data pipelines for model evaluation, prompt testing, and feature monitoring.
• Build internal tooling, SDKs, and abstractions to reduce toil for ML Engineers and accelerate the path from prototype to production.
• Implement and uphold guidelines in code layering, asynchronous system build, and modular architecture for maintainable, testable codebases.
• Participate in pull request reviews and contribute to a culture of engineering quality and collaborative learning.
• Contribute to service releases, coordinate with feature teams, and support globally deployed systems with operational rigor.
• Help automate ML workflow steps such as evaluation harnesses, prompt pipeline testing, and LLM-as-a-judge tooling.
• Collaborate closely with machine learning developers and feature teams to understand requirements and translate them into well-scoped engineering solutions.

Requirements

• B.S. or M.S. in Computer Science or equivalent experience.
• More than 2 years of experience in production software engineering, focusing on backend services and infrastructure.
• Proficiency in Python, including writing clean, unit-tested, and well-documented code; familiarity with frameworks like Pydantic or LangChain is a plus.
• Experience crafting and implementing concurrent and asynchronous systems using Python, Node.js, or Go.
• Solid understanding of OOP principles, common patterns, and event-driven architectures.
• Strong grasp of reliability, observability, and clean modular design in data pipelines and service layers.
• Proficiency in cloud platforms (AWS, GCP, or Azure) and containerized deployment (Docker, Kubernetes).
• Strong communication skills and a collaborative approach to working across engineering and ML teams.

Preferred Qualifications

• Familiarity with integrating language models into feature pipelines, including timely engineering and vector search techniques.
• Experience in building and launching machine learning models for production environments.
• Experience working with the Agentic platform.
• Experience with or interest in MLOps tooling: experiment tracking, model lifecycle management, or evaluation frameworks.
• Exposure to CI/CD pipeline build, particularly in cloud or ML environments.
• Experience developing and maintaining RESTful APIs and reviewing client-service contract specifications.
• Familiarity with large-scale data processing technologies such as Kafka or Spark.
• Experience with monitoring and observability systems applied to distributed or AI-powered services.

Why Acrobat

Develop products that reach millions of users. Your code operates within Acrobat worldwide on cloud, desktop, and mobile platforms. The tooling and pipelines you build directly multiply ML Engineer output. Grow your skills at the frontier of applied AI, working alongside deep ML practitioners on GenAI and agentic systems. Collaborative, fast-paced team with real ownership and room to build technical direction as you develop.

What You’ll Need to Succeed

• Required Qualifications:
• Required Qualifications:

Expected Pay Range

The U.S. pay range for this position is $114,100 -- $214,950 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience.

State-Specific Notices

• California: Fair Chance Ordinances
• Colorado: Application Window Notice
• Massachusetts: Massachusetts Legal Notice