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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - **Company:** Adobe Systems - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $183,300.0 - $265,350.0 - **Contract:** Permanent contract - **Skills:** Adobe Acrobat, Adobe Illustrator, Adobe Photoshop, Adobe Creative Cloud, Adobe Photoshop Lightroom, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Computer Vision, Cloud Computing, Nvidia CUDA, Distributed Systems, Systems Analysis, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Service Discovery, Software Engineering, Data Logging, Pytorch, Large Language Models, Multi-Agent Systems, Generative AI, Adobe, Kubernetes, ONNX (Open Neural Network Exchange) Format, Variational Autoencoders, Machine Learning Operations, Virtual Agents, Network Server, Docker - **Published:** August 23, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-agentic-systems-san-jose-ca--fc8dad72-b8f1-4dc2-a7ce-b0057e2040fa ## About the Role * 5+ years of experience building and operating production software systems, including hands-on experience building or operating LLM-driven agents or agentic pipelines - not just using coding assistants * Strong understanding of agent architectures - reasoning, retrieval, tool use, and multimodal capabilities. * Familiarity with AI agent ecosystems and standards, such as MCP servers, APIs, and semantic retrieval * Experience designing or working with evaluation frameworks that measure the quality, reliability, and safety of AI-driven systems * Experience with retrieval-augmented generation (RAG) and agent memory or session-persistence systems * Strong distributed systems background: service discovery, secrets/credential management, and multi-tenant infrastructure on Kubernetes * Working familiarity with modern generative and computer vision architectures (Transformers, Diffusion models, GANs, CLIP, VAEs, MLLMs) sufficient to design systems that generate and validate code using them * Proficiency with core technologies such as Python, PyTorch, TensorFlow. Docker, Kubernetes, AWS * Nice to have: Familiarity with model serving and inference optimization tooling (e.g., NVIDIA Triton, TorchServe, ONNX, CUDA) and techniques such as quantization and pruning - useful for collaborating with the team's ML engineers, though not a core requirement for this role, Adobe Acrobat, Adobe Photoshop, Adobe Product Family, Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), CUDA (Compute Unified Device Architecture), Cloud Computing, Compensation and Benefits, Computer Architecture, Computer Vision, Continuous Improvement, Customer Experience, Customer Relations, Distributed Computing, Docker, Ecosystems, Incident Response, Integrated Circuits (ICs), MCP - Microsoft Certified Professional, Machine Learning, Machine Tool, On Call, Product Engineering, Production Support, Production Systems, Quality Management, Quality Metrics, Safety/Work Safety, Software Engineering, Standards Development, Systems Analysis, Systems Engineering, Testing ## Description Adobe is looking for a Senior Software Engineer (Agentic Systems) to help build an AI-driven development platform that moves new AI/ML and Generative AI capabilities from research into production across Adobe's flagship creative products. In this role, you will design and build the agents, orchestration systems, and evaluation infrastructure that translate cutting-edge research models and specifications into reliable, production-ready services - partnering closely with Adobe Research, product engineering, and platform teams. You will join a team responsible for the ML cloud services that power features used daily by millions of creators across products like Photoshop, Lightroom, Illustrator, Express, Stock, and enterprise surfaces. This is a hands-on senior IC role defining a new engineering discipline - building the systems that build software - with direct impact on how quickly AI capabilities reach customers. The Team Our team includes dedicated machine learning, software, ml-ops and dev-ops engineers from a diverse set of backgrounds who are encouraged to bring their experience, ideas, and creativity to produce the best possible outcomes. We operate with agility and learn from failure. The highly dynamic environment allows us to grow while we add new value and deliver impactful results. As a member of the team, you will interact with others who are genuine and want you to succeed. Your talent, experience, and voice are valued and will make a difference. What you'll do * Design, build, and operate an ecosystem of agents to build, optimize, evaluate and deploy research model specifications and inference pipelines into production-ready services * Design and maintain evaluation frameworks and infrastructure to continuously measure and improve agent quality, reliability, and safety * Build or integrate required new functionality with supporting platform infrastructure * Identify and resolve platform and workflow bottlenecks that limit how quickly AI-driven systems and agent-generated changes can be developed, reviewed, and deployed * Collaborate closely with Research, Product, and Engineering partners to define the standards (API schemas, testing, security) that generated code must meet * Ensure services meet production standards for observability, monitoring, logging, and incident response * Participate in on-call and production support, contributing to a culture of customer focused, operational excellence ## Related Videos - [Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [3x Performance: A Humbling Journey](https://www.wearedevelopers.com/videos/100165-3x-performance-a-humbling-journey) - [The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)