Senior Product Security Engineer

Adobe•San Jose, CA

1d•$144,800 - $261,450

About The Position

This role focuses on designing and delivering AI-driven security features, shaping platform architecture, and leading the team's approach to AI risks. The engineer will be responsible for evaluating LLM outputs, identifying security gaps, and guiding product security engineers, developers, and product teams. The position involves applying AI-assisted tools to enhance speed and quality, strengthening code quality through reviews and mentorship, and making defensible risk-based decisions to balance security, speed, and user experience.

Requirements

Bachelor's degree or equivalent practical experience in Computer Science, Engineering, or a related field.
6 or more years in software development, including full-stack or backend work and a record of owning features.
Command of Secure SDLC practices, application security, threat modeling, and how security reduces risk.
Fluency in Python and JavaScript, with React preferred, across AI workflows, data pipelines, and user-facing tools.
Hands-on experience building production AI systems with LLMs, prompt engineering, AI APIs, and retrieval-based systems.
A clear view of AI risks like prompt injection and hallucination, with experience evaluating outputs for quality and safety.
A record of design decisions that shape system architecture.
Familiarity with cloud platforms, preferably Azure, plus containers, CI/CD pipelines, Git, and modern workflows.
Skill in aligning teams, leading technical work, and explaining risk to technical and non-technical audiences.
Experience mentoring engineers and lifting security practices across a team.

Responsibilities

Own the design and delivery of AI-driven security features, from Azure OpenAI integrations to retrieval-augmented generation and context retrieval.
Shape the architecture of the team's platforms across React, Python FastAPI, Celery, Postgres, Redis, and Kubernetes.
Define how the team evaluates LLM and retrieval outputs so security analyses stay accurate and reliable.
Lead the team's approach to AI risks like prompt injection, data exposure, and output manipulation.
Spot gaps in security coverage and build capabilities that deepen findings across products.
Guide product security engineers, developers, and product teams as a technical partner.
Balance security, speed, and user experience through defensible risk-based decisions.
Align design and approach across teams as a technical lead.
Strengthen code quality through reviews, secure coding practices, and mentorship.
Apply AI-assisted tools like GitHub Copilot and Cursor to move faster while protecting quality.