Sr. Software Engineer, Agentic
uberMillbrae (CA)
About the role
Overview:
In this role you design, build, and operate production-grade agentic systems that improve complex operational workflows at scale. You collaborate with operators and domain experts to identify opportunities for AI agents and automation, turning ambiguous challenges into reliable software. You own the full lifecycle from discovery to deployment and continuous improvement, helping establish the technical foundations for broader adoption of agentic AI at Uber. Compensation / Benefits bonus program equity opportunities 401(k) plan various benefits
Responsibilities:
Partner with operational teams to map workflows and identify where AI agents, automation, or human decision-making add value Design, develop, and deploy production-grade agentic systems including AI agents, services, APIs, data pipelines, and human-in-the-loop workflows Prototype and iterate with real users, converting vague requirements into scalable software Design for reliability, scalability, and operational resilience with observability, tracing, logging, metrics, and alerting Own production lifecycle, troubleshoot, recover, and improve performance based on feedback Develop evaluation frameworks to measure agent effectiveness and ensure safety, compliance, and auditability Collaborate on reusable capabilities, platform components, and standardized integration patterns Contribute to architectural decisions, tooling, and standards to scale Agentic Pods across Uber
Key requirements:
Strong backend or distributed systems experience (AI-enabled apps, orchestration platforms, APIs, asynchronous workflows, or enterprise integrations) Hands-on ownership of production systems including monitoring, incident response, and recovery 2 years of specialized software engineering experience in languages such as C, C++, Java, Python, or Go (may be gained through education, employment, training, or coursework) Ability to translate complex operational challenges into technical solutions in collaboration with business stakeholders curiosity ownership collaboration across technical and non-technical teams agentic AI applications and LLM integration with tools, APIs, and enterprise data agent orchestration, evaluation frameworks, RAG, context management, model routing, AI safety controls site reliability engineering (SRE), infrastructure, platform engineering, or forward-deployed engineering
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