Software Engineer II - AI Platform
Uber
Job Overview
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Job Description
About the Role
The Uber Agent Platform team is building the platform for AI agents that run parts of the business. The mission is to provide best-in-class tools and infrastructure for creating, evaluating, debugging, and deploying high-quality, resilient multi-agent systems at scale.
Key Responsibilities
- Build core platform capabilities to support production-grade AI agents.
- Design evaluation frameworks and automated testing in CI/CD pipelines.
- Develop managed services including memory, chat history, and agent runtime environments.
- Create registries for agents, tools, and skills for ease of reuse.
- Enhance developer workflows for smooth transition from experimentation to production.
Basic Qualifications
Bachelor's degree in Computer Science or related field (or equivalent experience). Proficiency in Python or Go with at least 4 years software engineering experience in scalable systems, with strong system design, service reliability, and scalability experience. Excellent communication skills are vital.
Preferred Qualifications
Experience with end-to-end AI Agent development lifecycle using frameworks like LangChain, CrewAI, and AutoGen. Familiarity with LLM-based systems, prompt engineering, fine-tuning, or embedding-based retrieval frameworks is beneficial.
Compensation & Benefits
Base salary ranges from USD 171,000 to USD 190,000 annually for US locations with eligibility for bonus programs, equity awards, 401(k) plans, and various benefits. Additional details available at the provided benefits link.
Key skills/competency
- Software Engineering
- AI Platform
- System Design
- CI/CD
- Python
- Go
- Scalability
- Evaluation Frameworks
- Managed Services
- Multi-agent Systems
How to Get Hired at Uber
- Research Uber's culture: Study Uber's mission, values and recent news.
- Customize your resume: Tailor it to highlight AI platform experience.
- Emphasize technical skills: Focus on Python, Go and CI/CD expertise.
- Prepare for system design: Practice scalability and reliability scenarios.
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