Build reliable agent capabilities that turn advances in reasoning, planning, memory, and tool use into useful product experiences. This role spans agent systems, platform engineering, integrations, and full-stack product development.
Investigate developments in agent technology and identify valuable product opportunities.
Translate promising model behaviors into reusable skills and workflows that address user needs.
Build and improve Python agent infrastructure, including orchestration, context management, structured outputs, retries, and error recovery.
Design tools and integrations for email, calendars, messaging, browsers, documents, CRMs, and business software.
Own capabilities across Python agents, Django services, APIs, React interfaces, data models, background jobs, observability, and production operations.
Create platform abstractions that make capabilities easier to compose, extend, and maintain.
Improve agent execution latency, cost, reliability, and safety.
Work with evaluation and quality partners to define expected behavior and turn evaluation results into engineering improvements.
Analyze production traces and user feedback to identify and resolve underlying issues.
Guide technical decisions on ambiguous problems and contribute to design reviews.
At least 5 years of experience building production software systems end to end, including deployment and operational iteration.
Strong Python skills and hands-on experience with Django, APIs, React, and TypeScript.
Experience building or operating agent systems, including planning, tool calling, structured outputs, context and state management, retries, and orchestration.
Experience designing integrations with external APIs and business software.
Ability to turn loosely defined user needs and emerging technologies into practical product features.
Experience owning complex systems in production, including observability, debugging, and improvements based on production signals.
Skill in designing reusable platform abstractions and diagnosing issues across prompts, model outputs, application code, databases, and APIs.
Background in LLMs, prompt engineering, or production model evaluation is useful, as is experience optimizing high-volume systems for latency, cost, and reliability.
Demonstrated career progression within an organization or experience founding a startup, along with strong engineering fundamentals.
On-site in Palo Alto, California, with hybrid flexibility.
Build and scale reusable AI platform capabilities, backend services, APIs, and full-stack product features, connecting applied generative AI to modern web experiences while improving reliability and performance.
Build and own a governed agent-authoring platform that turns audit methodology into configurable, testable agents, with versioning, quality controls, and traceable production behavior.
Designs and operates full-stack intelligent applications that integrate AI/ML models into business workflows, with a focus on reusable document-processing solutions, model lifecycle management, and production reliability.
Lead the design and delivery of production AI systems, from agentic workflows and retrieval architectures to reliable product experiences, while contributing code, guiding engineers, and raising engineering standards.
Design and implement AI agents, automation workflows, and data solutions that streamline investment and corporate processes, integrating existing systems while improving data quality, reliability, and adoption.
