Lead AI Product Engineer - AI Agents & Emerging Products

System1•Los Angeles, CA

5h•Hybrid

About The Position

System1 is building the next generation of AI Agentâpowered consumer products, and weâre looking for a Lead AI Product Engineer to help lead the effort. Youâll design, build, and operate our most ambitious AI agent systems from early prototypes through production, owning core architecture, reliability, and performance as these products scale. This is a high-impact, hands-on engineering role for a builder who can move quickly from concept to working systems, shipping, iterating, and hardening real agent workflows that deliver consumer value at scale. This role sits at the intersection of multi-agent orchestration, agentic coding, and production engineering, offering the opportunity to work on cutting-edge AI while directly influencing real-world product outcomes and business performance. You do not need prior experience with every protocol or framework listed belowâwe value strong systems thinking, production experience, and the ability to learn fast in emerging domains.

Requirements

Strong experience building LLM-powered systems in production, including prompt engineering, structured outputs, tool/function calling, and agentic coding workflows where agents write, modify, and validate code within guardrails.
Solid backend engineering skills with Python and/or Node.js, designing APIs, async services, and background workers that support long-running, stateful agent workflows.
Experience designing multi-step, agent-driven execution loops (plan â act â evaluate â retry), with explicit state management, idempotency, and failure recovery.
Comfort with distributed systems primitives (queues, pub/sub, durable state, rate limiting, concurrency controls) required to coordinate agents at scale.
Proven ability to instrument and observe agent behavior, emitting structured events for task success, failures, retries, overrides, and code-generation outcomes.
Experience deploying and operating systems on cloud infrastructure (AWS or GCP), using Docker and CI/CD pipelines.
Familiarity with inter-agent communication patterns, including 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), with the ability to implement evolving specs pragmatically.

Responsibilities

Build and operate multi-agent systems that plan, coordinate, and execute tasks across multiple tools and services, to solve real user problems at scale.
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, with exposure to agent-to-tool payment flows as the ecosystem evolves.
Collaborate with Product & Design to translate user problems into shippable agent workflows, iterating quickly based on real-world feedback.