Fullstack Engineer (Data Platform)

Join our team as a Fullstack Engineer (Data Platform) and take ownership of the infrastructure that powers our cutting-edge gaming technology. You'll work directly with the founding team and have a significant impact on the direction of the company. Your responsibilities will include building preprocessing pipelines, optimizing inference in production, and owning orchestration and GPU clusters. We're looking for someone with a strong background in infrastructure and a passion for coding.

Either way, you're still writing code today and you want to keep writing it

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

About Medal.tv

What We're Looking For

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 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

USD 180,000 - 300,000

Responsibilities

• Conception, development, and optimization of preprocessing pipelines for transforming raw gameplay video into training-ready data.
• Ownership of orchestration and GPU clusters, including scheduling, utilization, and capacity management.
• Collaboration with the founding team to make technical decisions and contribute to the overall infrastructure strategy.
• You optimize inference in production - batching, quantization, KV cache, serving runtimes - and you own the latency and cost numbers rather than reporting them

Requirements

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