Full Stack Engineer

Adobe Systems
San Francisco, CA, United States
6 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Clean Code Principles Application Programming Interfaces (APIs) Artificial Intelligence Software Quality Continuous Integration Cursor (Graphical User Interface Elements) Software Product Management Software Tools Systems Integration Retrieval-Augmented Generation Large Language Models Prompt Engineering
+4 more
Backend Adobe Production Code Enterprise Integration

Job description

Experteer Overview In this role you will help build Adobe’s AI-native Agentic Product across desktop, mobile, web, and embedded surfaces. You’ll fuse LLM capabilities with agentic reasoning to deliver scalable, reliable experiences for millions of creators. You will shape engineering practices, tooling, and cross-platform delivery on a fast-moving AI product, acting as a hands-on builder and technical leader from model integration to production infrastructure. Join a ground-floor team shaping how engineering evolves with AI tools and agentic systems. Compensation / Benefits * Full-stack implementation across Agentic Product, including LLM integration, agentic layers, API surfaces, client runtimes, and platform-specific delivery * Production buildout of the agent to robust, scalable systems meeting quality and reliability bars * Develop high-leverage components: model orchestration, tool use, multi-step task execution, memory/context management, agent evaluation infrastructure * Promote AI-assisted engineering practices using Claude Code, Codex, and similar tools to accelerate delivery and elevate code quality * Contribute to developer experience: tooling, local development loops, CI/CD ergonomics, scaffolding for fast yet reliable shipping * Stay current with model capabilities and evaluate new LLM approaches, recommending adoption strategies * Help build and maintain evaluation/quality framework for agent behavior (evals, benchmarks, feedback loops) * Collaborate with leadership to translate product ambitions into technical delivery, surfacing constraints early * Work with platform, infrastructure, data, and security teams to ensure guardrails, data protection, observability, and scale readiness * Raise coding bar through high-quality code, effective reviews, and knowledge sharing Tasks * Solid hands-on experience with large language models and LLM APIs * Practical fluency with prompt engineering, context window management, retrieval-augmented generation, production LLM workloads * Experience shipping agentic systems (tool use, multi-step reasoning, orchestration, memory persistence, failure modes) * Hands-on experience with AI-assisted engineering tools (Claude Code, Codex, Cursor) and integrating into development workflows * Full-stack depth to contribute across backend services, client SDKs, and platform integrations * Strong craft: readable, production-quality code, testing discipline, local development ergonomics * Clear communication to discuss tradeoffs with PMs and senior stakeholders * Comfort in fast-moving, ambiguous environments where models/frameworks evolve rapidly * Cross-functional collaboration with product, design, platform, and security teams * Familiarity with quality, trust, reliability requirements for shipping AI features to large audiences including responsible AI considerations Key requirements *

Requirements

Experteer Overview In this role you will help build Adobe’s AI-native Agentic Product across desktop, mobile, web, and embedded surfaces. You’ll fuse LLM capabilities with agentic reasoning to deliver scalable, reliable experiences for millions of creators. You will shape engineering practices, tooling, and cross-platform delivery on a fast-moving AI product, acting as a hands-on builder and technical leader from model integration to production infrastructure. Join a ground-floor team shaping how engineering evolves with AI tools and agentic systems. Compensation / Benefits * Full-stack implementation across Agentic Product, including LLM integration, agentic layers, API surfaces, client runtimes, and platform-specific delivery * Production buildout of the agent to robust, scalable systems meeting quality and reliability bars * Develop high-leverage components: model orchestration, tool use, multi-step task execution, memory/context management, agent evaluation infrastructure * aaaaa to AI-assisted engineering practices using Claude Code, Codex, and similar tools to accelerate delivery and elevate code quality * Contribute to developer experience: tooling, local development loops, CI/CD ergonomics, scaffolding for fast yet reliable shipping * Stay current with model capabilities and evaluate new LLM approaches, recommending adoption strategies * Help build and maintain evaluation/quality framework for agent behavior (evals, benchmarks, feedback loops) * Collaborate with leadership to translate product ambitions into technical delivery, surfacing constraints early * Work with platform, infrastructure, data, and security teams to ensure guardrails, data protection, observability, and scale readiness * Raise coding bar through high-quality code, effective reviews, and knowledge sharing Tasks * Solid hands-on experience with large language models and LLM APIs * Practical fluency with prompt engineering, context window management, retrieval-augmented generation, production aG _ workloads * Experience shipping agentic systems (tool use, multi-step reasoning, orchestration, memory persistence, failure modes) * Hands-on experience with AI-assisted engineering tools (Claude Code, Codex, Cursor) and integrating into development workflows * Full-stack depth to contribute across backend services, client SDKs, and platform integrations * Strong craft: readable, production-quality code, testing discipline, local development ergonomics * Clear communication to discuss tradeoffs with PMs and senior stakeholders * Comfort in fast-moving, ambiguous environments where models/frameworks evolve rapidly * Cross-functional collaboration with product, design, platform, and security teams * Familiarity with quality, trust, reliability requirements for shipping AI features to large audiences including responsible AI considerations Key requirements *

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