The OpportunityAdobe 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.
What You'll Do- 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.
What You'll Need to SucceedRequired Qualifications
- 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.
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 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.
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.
Expected Pay Range:Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. 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. Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $148,500 - $214,950In Washington, the pay range for this position is $135,100 - $195,550
At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).
In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
