Adobe
Senior AI Platform Engineer
San Jose, USA · Senior
Sponsorship not specified$152k-$265kDetected 9 hours ago
TypeScriptPythonJavaGoC++ExpressDistributed SystemsVector DatabasesPlatform EngineeringLLMsRAGAgentic AIAI OrchestrationResearchCommunicationCollaboration
About the role
- Adobe empowers individuals and organizations to create exceptional content effortlessly.
- The AI for Engineering team builds a scalable, production-grade AI platform that powers creativity across design, imaging, motion, and personalization.
- We are seeking a Staff Engineer - AI for Engineering to design and build the next-generation AI systems that bring intelligent, adaptive, and agentic experiences to Adobe Express.
Responsibilities
- Design and implement scalable orchestration layers that coordinate LLMs, tools, APIs, memory stores, and multi-step reasoning workflows.
- Build production-grade agent frameworks that support planning, task decomposition, tool invocation, multi-agent collaboration, and persistent memory.
- Develop high-performance inference and runtime systems with strong guarantees around latency, reliability, observability, and cost efficiency.
- Design evaluation and feedback systems that measure reasoning quality, task success, hallucination rates, and agent behavior - enabling rapid iteration and continuous improvement.
- Design data flows, session-level intelligence, and contextual memory systems that allow agents to operate coherently across interactions.
- Partner with applied research, product, and platform teams to bring intelligent agentic capabilities into real developer-facing experiences.
- Drive architectural strategy for AI for Engineering - connecting models, reasoning engines, tools, and data streams into adaptive AI systems.
- Mentor senior engineers in modern AI system design, LLM orchestration patterns, and agent platform architecture.
Requirements
- 10+ years of experience building large-scale distributed systems, AI platforms, or intelligent service architectures.
- Proficiency in TypeScript, Python and at least one systems language (Java, Go, C++), with experience building production AI services.
Nice to have
- Experience architecting AI assistants, copilots, or agent platforms in production environments.
- Experience working with multimodal generative systems (text, image, video, motion).
- Familiarity with tool-augmented LLM systems, RAG architectures, vector databases, and contextual memory systems.
- Exposure to evaluation frameworks for generative AI quality and safety.
- Experience contributing to open-source AI frameworks or publishing technical thought leadership.
- We're on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact.
- The next big idea could be yours.
- Let's Adobe together
Skills
- If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
Compensation
- 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 $151,800 -- $265,350 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 $183,300 - $265,350
- 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.
Benefits
- In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
Company info
- A core part of this role is building scalable Agentic AI platforms that empower developers, significantly boost engineering productivity, and accelerate AI adoption across the organization.
This listing is sourced directly from Adobe's careers page and normalized into a canonical job model.