Fullstack Engineer – Data Platform

General Intuition is the frontier lab for acting in space and time. We build large action models and world models that can perceive, predict, and act across virtual and physical environments. General Intuition builds on the strength of Medal, the world's largest and fastest-growing platform for gaming clips, where millions of gamers capture, share, and discover new games every year. We've raised over $650M from Khosla, GC, Valor, and Point72 since October 2025, and recently closed our latest round at a $6.2B valuation.

Billions of gameplay clips a year come in on one side. Large action models and world models train and serve on the other. Everything in between - the pipelines that turn raw footage into training data, the clusters that consume it, the storage and I/O that keeps them fed, the runtime that serves the results - is infrastructure, and it is what you own.

This is deliberately not a narrow role. We are not hiring a Kubernetes specialist, or a data engineer, or an inference person. We're hiring someone who can move from cluster scheduling to disk throughput to a preprocessing pipeline to inference latency in the same week, and who becomes the technical reference other engineers bring their system designs to, across both GI and Medal.

We weigh two routes in the same. Either you spent years deep in infrastructure at a large tech company or a serious lab, then left to build your own thing as founder, co-founder, or founding engineer, and you've been at it for at least a year. Or you've spent five or six years going deep on hard infrastructure inside a big company or lab, you own a system people have heard of, and you're ready for a place where you decide what gets built. Either way, you're still writing code today and you want to keep writing it.

You'll work directly with the founding team, at a company small enough that the decisions are yours to make.

• You own orchestration and GPU clusters - scheduling, utilization, capacity. Expensive hardware sitting idle is your problem, and so is a training run blocked behind the scheduler.

• You build the preprocessing pipelines that turn a very large corpus of raw gameplay video into training-ready data, at a throughput that keeps training from waiting on data.

• You treat disk and network I/O as a first-class constraint rather than an afterthought. At our data volumes it is frequently the bottleneck, and you know how to find out whether it is.

• You optimize inference in production - batching, quantization, KV cache, serving runtimes - and you own the latency and cost numbers rather than reporting them.

• You are at ease across cloud providers and comfortable owning infrastructure as code, multi-region deployment, and the reliability of everything above.

• You can draw a circle around something substantial and say: this was mine. You decided how it was built, you chose the technologies, and you carried it to production. Not "contributed to a team that" - you made the calls, and you have several examples.

Kubernetes, multi-region. GPUs across cloud providers. Python and Go, with Rust and C++ where performance demands it. Terraform. In-house frontier models: action models, world models, video understanding.

We are not dogmatic about any of this. If you think we've made the wrong call somewhere, that's a conversation we want to have in the interview.