ML Engineer – Gen AI Assistant & Experience Engineering

Adobe•San Jose, CA

About The Position

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! Adobe Experience Cloud | Gen AI Experience Engineering About the Team The Gen AI Experience Engineering team is defining the future of intelligent workflows across Adobe Experience Cloud with the AI Assistant. We operate like a startup within Adobe—fast, iterative, and customer-focused. Our mission is to build the intelligent systems, models, and services that power Adobe’s AI Assistant experience. The Opportunity We’re looking for an AI/ML Engineer who is excited to build intelligent features using whatever approach solves the problem best. Some solutions will use LLMs and generative AI, some will rely on traditional ML, some will be heuristic or rules-based, and many will be hybrid systems that combine them. Your job is to evaluate the problem, choose the right approach, and deliver high-quality, scalable solutions that improve user experiences. In this role, you’ll build models, design prompts, develop services, run evaluations, and ship features end-to-end. This is a hands-on applied engineering role with broad ownership, where you’ll work across modeling, service development, and lightweight ops.

Requirements

5+ years experience in machine learning, applied AI engineering, IR or full-stack intelligent feature development – prioritizing experience with core ML problem-solving, such as building/designing or debugging predictive models (over operational pipelines and monitoring)
Hands-on experience with both LLM-based and traditional ML techniques, and the judgment to choose the right tool for the job.
Strong software engineering fundamentals and experience building production services (Node, Python, TypeScript, Go, or similar).
Ability to design evaluation frameworks, run experiments, and iterate rapidly.
Comfortable owning features from prototype → production, including monitoring and optimization.
Excellent communication and collaboration skills; thrives in environments with ambiguity and autonomy.

Nice To Haves

Experience with hybrid LLM + deterministic systems, vector search, or orchestration tools.
Knowledge of Adobe Experience Cloud or other enterprise SaaS ecosystems.
Contributions to open-source AI/ML tools, model-serving frameworks, or evaluation libraries.
Prior startup or high-velocity product development experience.

Responsibilities

AI, ML & Hybrid Solution Development Build and iterate on solutions using the full spectrum of approaches: LLMs, classical ML, heuristics, rules engines, retrieval systems, or combinations thereof.
Design, train, and evaluate classical ML models where appropriate, and integrate, fine-tune (via prompting or adapters), and evaluate partner-provided LLMs for generative AI, classification, search, and content understanding use cases
Develop prompting strategies, multi-step prompt workflows, and agents that power interactive AI experiences.
Build hybrid pipelines that combine deterministic logic with AI/ML components for predictable, reliable outcomes.
Service & Feature Engineering Implement backend services and inference pipelines for the AI Assistant across Experience Cloud.
Build RAG systems, model-serving layers, experimentation hooks, and scalable APIs.
Partner with frontend engineers and product teams to turn concepts into shipped features.
Evaluation, Data, and Light Ops Build automated evaluation pipelines to measure quality, safety, latency, and reliability.
Prepare datasets for evaluation, fine-tuning, and experimentation.
Deploy models and services using CI/CD, containers, and cloud workflows.
Monitor performance and iterate quickly based on data and user signals. While this is not a dedicated ops role you should be able to own and operate your work end-to-end as projects require.
Cross-Functional Collaboration Work closely with Product, Engineering, Design and ML teams to explore new ideas and deliver customer-facing features.
Help define best practices for AI/ML development, evaluation, and hybrid system design.
Contribute to shared tools that accelerate experimentation and improve developer productivity.