Software Development Engineer, AI Platform

Adobe•San Jose, CA

1d•Hybrid

About The Position

The Opportunity Adobe Document Cloud’s AI team is building the next generation of AI-powered features powering Acrobat AI Assistant spanning billions of PDFs and millions of transactions monthly. We’re looking for a Software Development Engineer to help build and maintain the backend services, tooling, and pipelines that enable our Machine Learning Engineers to develop, evaluate, and ship production-ready AI features at speed. This role ideal for someone with solid backend fundamentals who wants to grow their experience working at the intersection of software engineering and applied AI. You’ll contribute directly to the systems that power features like question-answering, document summaries, suggested questions, and attribution. All deployed across cloud platforms, desktop environments, and mobile devices at global scale.

Requirements

B.S. or M.S. in Computer Science or equivalent experience at a similar level.
More than 2 years of experience in production software engineering, concentrating on backend services and infrastructure.
Proficiency in Python, including writing clean, unit-tested, and well-documented code; familiarity with frameworks such as 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 (encapsulation, inheritance, polymorphism, abstraction) and common patterns used to build software (Singleton, Factory, Observer, Strategy).
Solid grasp of event-driven architectures and non-blocking I/O operations.
Proficiency writing unit and integration tests; strong debugging skills across service boundaries.
Familiarity with 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.

Nice To Haves

Familiarity with integrating language models into feature pipelines, including timely engineering and vector search techniques.
Experience in building and launching machine learning models for use in 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.

Responsibilities

Design, build, and maintain scalable backend services and APIs that support Acrobat AI Assistant features and the ML pipelines that power them.
Develop and maintain data pipelines for model evaluation, prompt testing, and feature monitoring — with an emphasis on reliability, observability, and clean modular design.
Build internal tooling, SDKs, and abstractions that 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.