Staff Software Engineer - AI-Driven Full-Stack & ML

URBN (Urban Outfitters, Anthropologie Group, Free People & Nuuly)

Philadelphia (Philadelphia County)

On-site

USD 140,000 - 190,000

Full time

14 days+

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Benefits offered by this job

Medical, dental, vision

Paid time off

Employee discounts

Retirement savings plan

Job summary

URBN is hiring a Staff Software Engineer to join the development of AI-powered digital experiences. We are looking for an experienced full-stack Engineer to join our mission of integrating algorithmic solutions with creative tools.

You will collaborate with a cross-functional team to deliver significant business impact across our digital ecosystem. Role involves building MCP servers, agentic workflows, and AI-enabled microservices with a strong emphasis on scalable architectures, reliability,

Qualifications

AI-Augmented Development: leverage AI-powered tools to accelerate coding while upholding quality and security.
RESTful APIs & Distributed Systems: design scalable APIs and distributed architectures.
System Integration & AI Workflows: integrate AI/ML services into backend systems.
Containerization & Cloud: Docker and cloud deployment, serverless and orchestration.
Infrastructure as Code: Terraform for reproducible, version-controlled environments.
Database Management: relational DB design, pooling and performance tuning.
Quality & Automation: CI/CD, TDD and automated code quality standards.
10+ years of experience in a Software Engineering role.

Responsibilities

Design and development of MCP servers for agentic tools using FastMCP or similar technologies.
Design and development of agentic workflows using ADK, A2A, LangGraph, or similar technologies.
Design and development of microservices using FastAPI, GraphQL, or similar technologies.
Develop functional designs while interacting with architects, partners, and end users.
Collaborating with Designers, Product Managers, and other Engineers to deliver user-facing products.
Conduct exploratory PoCs to assess deployment architectures and new data engineering tech.
Promote pathways for algorithm integration and productization with data flywheels.
Collaborate to implement and maintain ML architecture, data pipelines and training/inference in production.
Foster strong cross-functional partnerships.