Plsn Research Engineer, Inference responsible for deploying AI models and optimizing inference infrastructure. Focus on latency, throughput, cost, and model quality improvements.
Responsibilities
Own the path from a research checkpoint to a deployed model. Build the serving infrastructure and make new models straightforward to ship.
Profile and improve latency, throughput and cost across the inference stack. Measure the bottlenecks, make a change, and prove that it helped.
Evaluate quantization, distillation, KV-cache optimization and batching where they fit the model. Keep correctness and model quality in view as you optimize.
Build and tune serving systems for low-latency, real-time and streaming workloads. Own scheduling, memory use and GPU utilization.
Write or tune GPU kernels where the existing stack falls short. Work from model architecture down to the kernel and back up to orchestration.
Build tools that let researchers deploy models quickly and safely. Make performance regressions visible before a model ships.
We are a small team with a high bar and very little process. You get a real problem and the trust to go after it, and you decide how it gets solved. We think carefully and move fast.
When you build something here, you own it. The system, the results, and what happens when it breaks. Nobody else is going to pick it up for you.
Qualification
A Research EngineerA builder with public work to showHigh agencyTapped inYou care how and why models workFast to learnYou thrive in small
Required
A Research Engineer, Inference who thinks from first principles and builds things that work outside the lab. You care about why people want something and why models behave the way they do, and you care that what we ship works when it actually matters. If you have built products, shipped software, talked to customers or run outreach, and you are drawn to the hardest reliability problems in AI, this is the role.
This is a role for someone early in their career who has already built real things. We care more about how fast you have been learning than where you have been.
A builder with public work to show. Repos, hardware, a product, a paper, a competition result, a project or company you ran at school. We will read it, so make it good.
High agency. You want to build a company from the ground up. You start things without being asked, you finish them, and you take 100% ownership of the systems you build.
Tapped in. You know what is happening in the tech ecosystem around you and around the world. You spend time on X, you follow what people are building, and you know and connect with the people doing it.
Comfortable working from first principles. You can define a problem, talk to the people who have it, and build the minimal system that proves what is possible.
You care how and why models work, and you care that they actually work when the stakes are real.
Fast to learn. Your last year looks very different from the year before it.
Solid in Python and at home with ML tooling. You do not have to be a researcher.
You thrive in small, focused teams that value autonomy, speed and tight collaboration over process.
You think AI for critical systems is one of the most consequential problems in the field right now and you want to be early to solving it.
Students and recent graduates are welcome. We do not require a degree or a set number of years of experience.
