Product Security Engineer

Adobe•Seattle, NY

1d•Onsite

About The Position

Adobe's Security Partnership Product Engineering (SPPE) team is hiring a mid-level engineer to build AI-powered platforms that help secure their products. The team's tools include a threat modeling capability that delivers analysis in seconds and a new feature that surfaces risk hotspots across Adobe's product portfolio. The engineer will design AI-driven features, partner with security and engineering teams Adobe-wide, and make risk-based decisions that balance security with engineering velocity. This role is based in New York City, San Jose, or Seattle.

Requirements

Bachelor’s degree or equivalent practical experience in Computer Science, Engineering, or a related field.
4 to 6 years of software development experience, including full-stack or backend systems.
Solid foundation in Secure SDLC practices, application security, and threat modeling.
Proficiency in Python and JavaScript, with React experience preferred.
Experience with AI systems, including LLMs, prompt engineering, AI APIs like Azure OpenAI, and vector databases or retrieval-based systems.
Understanding of AI-specific risks like prompt injection and hallucination, with experience evaluating AI outputs.
Familiarity with cloud platforms, preferably Azure, and containerized deployments.
Knowledge of CI/CD pipelines, Git, and modern development workflows.
Ability to work independently and across teams, with judgment to make risk-based decisions and communicate technical risk clearly.
Curiosity about emerging AI and security techniques and a willingness to apply them.

Responsibilities

Build security analysis capabilities using LLM integrations with Azure OpenAI, prompt engineering, retrieval-augmented generation, and vector-based context retrieval.
Develop and maintain platforms end to end using React, Python FastAPI, Celery, Postgres, Redis, and Kubernetes with Argo.
Evaluate LLM and retrieval outputs to ensure accuracy and reliability for internal users.
Address AI-specific risks like prompt injection, data exposure, and output manipulation.
Make architecture decisions for new security capabilities, balancing performance, scalability, maintainability, and responsible AI use.
Identify security gaps with internal users and propose features to close them.
Partner with security and product teams to translate their needs into scalable features.
Use AI-assisted tools like GitHub Copilot and Cursor to move faster without compromising code quality.
Share knowledge with peers, support teammates, and contribute to Adobe’s security community.