Engineering
Runpod is the AI Developer Cloud. More than one million developers, from indie researchers to teams running frontier models in production, use Runpod to experiment, train, fine-tune, deploy, and scale AI on one platform. The platform has processed more than 20 billion inference requests. We closed a $100M Series A in June 2026. We're at an inflection point for AI infrastructure, and we're building the platform the next generation of developers will depend on.
We're a small, remote-first team. We take ownership seriously, move fast, and ship work that more than a million developers rely on every day. We're looking for people who care deeply, build with urgency, and want to matter at scale.
Learn more in our CEO's funding announcement: https://www.runpod.io/blog/one-million-developers.
We're looking for a ML Systems Engineer, Inference. We want Runpod to be the best place in the world to run LLM inference, meaning the fastest and the most cost-efficient. You'll lead that effort. You'll own LLM serving performance end to end. That means measuring it, understanding it, and improving it across models, hardware generations, and workloads. The work you ship will show up directly in the latency and cost our customers experience. This is a hands-on engineering role for someone who likes finding the real bottleneck and fixing it, then turning that fix into something that runs reliably in production.
RESPONSIBILITIES
- Define how we measure inference performance, including throughput, time to first token, inter-token latency, and cost per token, and build the tooling