Product Manager, Platform

IFS•Palo Alto, CA

2h•$130,000 - $150,000•Hybrid

About The Position

We are building intelligent, autonomous AI agents that can reason, plan, and act across real-world workflows. These agents go beyond chat; they take meaningful action, work across tools and systems, and operate in complex, high-stakes environments. As our Product Manager, you will lead the definition and delivery of early use cases for agentic AI. You will turn cutting-edge agent capabilities, such as long-term memory, tool use, and planning, into applied, valuable products. You will be use-case driven, but platform-aware, working closely with engineering and research to ensure what we build is feasible, scalable, and aligned with the underlying agent architecture.

Requirements

5+ years of product management or startup experience building technically complex, user-facing systems
Strong intuition for applied use cases — especially in automation, operations, or real-world workflows
Familiarity with agentic AI patterns and tooling (e.g., LLM agents, tool use, LangChain, OpenAI functions, memory systems)
Experience working closely with engineers and researchers on system-level product decisions
Strong communication, prioritization, and execution skills

Nice To Haves

Exposure to or interest in domains like logistics, field ops, manufacturing, or systems integration
Background in AI/ML infrastructure, developer platforms, robotics, or IIoT
Experience designing for human-in-the-loop or semi-autonomous systems
Hands-on experience with prototyping workflows or agents using modern frameworks (e.g. LangGraph, AutoGen)

Responsibilities

Identify and prioritize agent use cases with high ROI: operational automation, coordination agents, internal task delegation, etc.
Work with users and stakeholders to understand their workflows, challenges, and trust boundaries.
Prototype and iterate agent behaviors in production-like environments to validate fit and reliability.
Define the agent experience — from delegation to monitoring to escalation — for real-world scenarios.
Translate use cases into clear product specs: user flows, system interactions, prompts, safety requirements, and interfaces.
Own the roadmap and ship iteratively: starting small, learning fast, scaling wisely.
Collaborate with engineering to align product needs with agent platform capabilities (planning, memory, tool orchestration, retries, etc.).
Partner with AI research to bring experimental features into real-world settings.
Ensure the platform supports the complexity of applied, multi-step, tool-using agents.
Define success metrics around outcomes — time saved, accuracy improved, failures recovered — not just clicks or sessions.
Build product foundations that allow for vertical reuse, system generalization, and long-term scalability.