Introduction

Plato is an applied research lab building the foundational infrastructure to train specialized AI agents.

We turn real-world data streams into high-fidelity simulated environments that generate the training signal needed to make capable models. Our work supports frontier labs, startups, and enterprises building AI systems for complex, high-stakes work.

Today, only a handful of players can train models for capable work. Compute and algorithms are rapidly commoditizing, but reinforcement learning data remains the bottleneck. Plato is changing that by automatically scaling training environments from proprietary real-world data.

Why This Role Matters

Plato's product is the interface between frontier AI research and real-world deployment.

Our systems are used by labs, startups, and enterprises building AI agents for complex, high-stakes work. The research only matters if it becomes a product that technical teams can understand, trust, operate, and build on.

As a Member of Technical Staff, Product Engineer, you will own the end product: the workflows, interfaces, backend systems, and product primitives that turn Plato's research loop into usable software for customers and internal teams.

Role Description

You will build 0->1 products at the edge of AI research, customer workflows, and production engineering.

This role is for someone who likes turning ambiguous problems into real products: talking to users, understanding messy technical workflows, designing the right abstraction, building the full stack, and iterating quickly as the research and customer needs evolve.

You might build product surfaces for labs to inspect and validate generated environments, workflows for startups to turn real-world traces into training tasks, tools for enterprises to evaluate agent behavior, or internal systems that help researchers understand rollouts, failures, rewards, and telemetry.

You will stay close to the research loop, but your center of gravity is the product: making powerful systems usable, legible, and dependable.

You Will Work On

• Build end-user product surfaces for labs, startups, and enterprises working with Plato's RL environment generation platform.

• Turn ambiguous customer and research workflows into clear, usable, technically durable product experiences.

• Design and implement full-stack systems across frontend, backend, data models, observability, and internal tooling.

• Create interfaces for inspecting traces, tuning scenarios, validating environments, reviewing rollouts, and understanding model behavior.

• Work closely with researchers and customers to prototype quickly, learn from usage, and turn rough ideas into durable products.

• Ship high-quality software in a fast-moving, deeply technical team.

What We're Looking For

We're looking for a product-minded engineer who wants to build the end product while staying close to the frontier research loop.

You may be a strong fit if you:

• Enjoy going from 0->1 on new products, especially in ambiguous or fast-changing problem spaces.

• Have strong product taste and can make complex technical workflows feel simple and usable.

• Are comfortable owning full-stack product work across frontend, backend, data, and systems.

• Like talking to users, understanding their workflows, and translating that into software that solves real problems.

• Can operate with high agency when the product, customer need, and technical architecture are still being discovered.

• Care about correctness, craft, observability, and iteration speed.

• Want to build software that is part of the core AI training loop, not a wrapper around it.

How We Work

Being an engineer at an early-stage AI startup is not easy. These are the values we care about.

Ownership

We value teammates who bring novel ideas to the table, experiment, and see results through end to end. You'll have access to massive compute budgets to test large scale experiments.

Move Fast, Build Durable

Demand is growing faster than our team. We move quickly, prioritize ruthlessly, and ship systems that keep working under load.

Reality Over Narratives

Training data is incredibly fragile and prone to reward-hacking. We prioritize digging deep through data, manually if we have to, to garner deep intuition on retaining high quality throughput.

Stay Close to the Frontier

New AI capabilities rapidly change how we think about problems and what doors open. We stay close to the frontier of model capability, and encourage teammates to constantly share new findings and update their world model of what's possible.