Machine Learning Engineer

Adobe•San Jose, CA

8d•$190,200 - $345,650

About The Position

This is a position where you will build and launch a new product within Adobe working in an exciting org that is incubating next-generation ideas. This role is an outstanding opportunity to operate as a startup founding engineer while having the resources of an innovative, large technology company. Build the next generation agentic AI platform working with other engineers and designers to bring the new Adobe product to life. We are looking for a Staff Backend Engineer with 10+ years of experience in enterprise software development using Machine Learning and Agentic AI systems to help design and scale the infrastructure behind intelligent, autonomous applications. You’ll work at the intersection of backend engineering, applied ML, and AI agent orchestration. Your role includes building APIs, data pipelines, and runtime frameworks for intelligent workflows. If you enjoy solving complex distributed systems challenges, experimenting with innovative ML/LLM techniques, and pushing the boundaries of how AI agents interact with humans and software, this role is for you.

Requirements

BA/BS or MS degree in Computer Science, Engineering, or equivalent experience.
Strong backend engineering skills with Python
Proficiency in Agentic AI development
Proficiency in building APIs, distributed systems, and data pipelines.
Hands-on experience with LLMs, vector databases (Pinecone, Weaviate, FAISS, Milvus), and ML deployment frameworks.
Understanding of agentic AI concepts (tool calling, memory, planning, reasoning).
Familiarity with ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex.
Experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker, Kubernetes).
Solid knowledge of databases (SQL and NoSQL) and event-driven architectures.

Nice To Haves

Familiarity with LangGraph, AutoGen, or other agentic frameworks.
Contributions to open-source ML or AI infrastructure projects.
Exposure to MLOps tooling (MLflow, Weights & Biases, Ray, Prefect, Airflow).
Background in security for AI systems (guardrails, prompt injection defense, data privacy).
Experience with reinforcement learning, multi-agent systems, or fine-tuning LLMs.

Responsibilities

Architect, implement, and scale backend services that support AI applications powered by machine learning and autonomous agents.
Design APIs and microservices that integrate LLMs, vector databases, and knowledge retrieval systems.
Build infrastructure for AI agents (task planning, tool use, orchestration, state management).
Ensure backend systems are reliable, performant, and secure at scale.
Develop monitoring, logging, and observability for agent behaviors and ML pipelines.
Work closely with product and design teams to translate requirements into scalable backend solutions.
Stay ahead of with emerging research in LLMs, multi-agent systems, and autonomous AI.