Full Stack Engineer

About the Role

We’re looking for a Full‑Stack Engineer with strong backend and Dev Ops background to help build and evolve internal tools that power AI research and evaluation. This role sits at the intersection of backend architecture, infrastructure automation, and developer experience — with the larger goal of improving how we measure and ensure the quality and safety of AI systems. You’ll collaborate closely with machine‑learning researchers, data scientists, PMs, and engineers, translating complex research workflows into robust, scalable systems.

While your primary focus will be on backend services, data models, and infrastructure, you’ll also occasionally contribute to frontend development to help shape cohesive, user‑friendly interfaces that bring research workflows to life.

• Designing and implementing backend APIs and data models using modern web frameworks (e.g. Laravel, Ruby on Rails, Node.js / Adonis, or Python / FastAPI)
• Managing Mongo
  
  DB schemas and performing safe data migrations for live research datasets
• Building and maintaining Dockerized applications and CI/CD pipelines in Kubernetes / Argo CD environments
• Maintaining and versioning APIs and SDKs following Semantic Versioning (Sem Ver), and communicating updates clearly to internal users
• Working directly with internal customers to understand workflows, gather requirements, and translate them into actionable engineering tasks
• Automating developer processes and improving tooling and developer experience across the platform
• Occasionally contributing to frontend development (React or similar frameworks) to integrate new backend features or streamline researcher‑facing workflows

• Strong backend development experience with at least one modern web framework (Laravel, Rails, Node.js / Adonis, or Python / FastAPI)
• Proficiency with Docker and containerized application development
• Experience with Mongo
  
  DB and schema evolution in production environments
• Familiarity with Kubernetes, Argo CD, or similar Dev Ops platforms
• Understanding of CI/CD, API versioning, and SDK publishing workflows
• Excellent communication and collaboration skills — comfortable engaging with internal users, clarifying requirements, and turning feedback into practical solutions
• A customer‑first mindset and enthusiasm for improving internal tooling and developer experience
• A strong work ethic, eagerness to learn new systems and technologies, and the ability to connect disparate tools and architectures into cohesive solutions
• An open, reflective approach — you learn from past experiences without being biased toward past solutions
• Bonus: experience or curiosity in machine‑learning evaluation, data visualization, Amazon S3, or authentication and security best practices

You’ll help bridge the gap between research and engineering, enabling teams to evaluate and iterate on AI models more efficiently and responsibly. Your work will directly contribute to improving the quality, reliability, and safety of AI systems — not by training models, but by building the infrastructure that helps everyone understand them better. This is an opportunity to shape the tools that shape research itself — within a small, collaborative team that values thoughtful design, experimentation, and real‑world impact.

The U.S. pay range for this position is $153,600 – $286,600 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. 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.

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