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Lead AI Product Engineer - AI Agents & Emerging Products

Lead the design, build, and operation of multi-agent AI systems from prototypes to production, owning architecture, reliability, and performance for consumer AI products. Requires strong production experience with LLM-powered systems, agentic workflows, and backend engineering.

Los Angeles, CAML EngineeringOnsite

About the role

Responsibilities

  • Build and operate multi-agent systems that plan, coordinate, and execute tasks across multiple tools and services
  • Implement agentic coding workflows where agents generate code, run tests, create diffs or PRs, and iterate autonomously within guardrails
  • Design agent orchestration layers (planners, supervisors, evaluators) to manage task decomposition, retries, and long-running execution
  • Instrument and monitor agent behavior across success, failure, latency, cost, and human overrides
  • Collaborate with Product & Design to translate user problems into shippable agent workflows

Requirements

  • Strong experience building LLM-powered systems in production, including prompt engineering, structured outputs, tool/function calling, and agentic coding workflows
  • Solid backend engineering skills with Python and/or Node.js, designing APIs, async services, and background workers
  • Experience designing multi-step, agent-driven execution loops with explicit state management, idempotency, and failure recovery
  • Comfort with distributed systems primitives (queues, pub/sub, durable state, rate limiting, concurrency controls)
  • Proven ability to instrument and observe agent behavior with structured events
  • Experience deploying and operating systems on cloud infrastructure (AWS or GCP), using Docker and CI/CD pipelines
  • Familiarity with inter-agent communication patterns (message schemas, shared memory, blackboard systems, or event-driven coordination)
  • Experience implementing agent evaluation pipelines (prompt regression tests, simulation-based evals, automated scoring, human-in-the-loop review)
  • Understanding of cost-aware execution for agents (dynamic model routing, caching, batching, budget constraints)
  • Experience with RAG architectures, vector databases, and persistent memory strategies
  • Exposure to emerging agent protocols (e.g., x402 payment flows, MCP)

Benefits

  • Competitive salary + bonus + equity
  • Generous PTO + 11 company holidays
  • Open sick time
  • 100% covered Medical, Dental, Vision for employees
  • 401k with match
  • Health & Dependent Care Flex Spending Account
  • Paid professional development
  • Leadership & growth opportunities
  • Virtual company and team building events

Skills

PythonNode.jsLLMsPrompt EngineeringRAGVector DatabasesAWSGCPDockerCI/CD

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