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We firmly believe that the future of AI depends on the unglamorous: rethinking and rebuilding the stack, all the way down. From the hardware underneath it to the compiler targeting it and the cloud running it. Right now those three things fight each other and that friction shows up as a tax on every researcher trying to build something ambitious. We're here to axe that tax and make compute faster, cheaper and more available. When we succeed, compute will be portable enough that "which cloud, which chip" stop being something you worry about.
To achieve this, we are building our Kernel Optimizer, which takes code and finds its fastest possible form for whatever vendor and cluster topology you point it at, automatically, as well as the Model Foundry which manages the runs, makes research easier and moves workloads across clouds and chips as prices and availability ship, instead of leaving you locked into whatever vendor you signed with first.
We're backed by Susa Ventures, Y Combinator, along with some great funds and angels including Max Mullen and Paul Graham, as well as founders and executives at Neuralink, Notion and AMD. We're looking for researchers, engineers and organizations who agree with the basic premise: you don't get the next leap in AI without a leap in compute first.
We build the fastest GPU compiler in the world. Most compilers have to preserve correctness at every transform, constraining how far they can search, while we prove correctness at the end instead, allowing us to search a far wider space, with agents, with RL, with anything that works and still guarantee the result. It's why we hold #1 on NVIDIA's own kernel benchmark across hundreds of production kernels.
Speed at the kernel layer is only worth what we do with it though and our enterprise offering promises we'll turn a customer's dataset into a specialist model in days. To do that we run SFT, RL, DPO, design evals and distillation down to smaller and edge-deployable models. We're hiring a Member of Technical Staff to own the modeling side of that end to end.
The infrastructure you'll be working on top of is unusually strong, which is the only reason we're able to train models so fast. Every experiment goes through Model Foundry, which let's you run experiments on the best hardware, look at overviews or dive deep into any detail all in a versioned and reproducible manner, making it trivial to reuse and modify recipes. Underneath that is the most powerful engine you could ask for, which has been used to post-train multi-100B language parameter models on TPU, post-train robotics models on Trainium and pre-train AlphaFold v3 on AMD.
You'll own the post-training pipeline end-to-end: data curation, SFT, preference optimization, RL, evals, distillation and finally deployment
You'll design reward functions and RL looks along customer domain experts, who know the task inside-out but not our training stack
You'll build an eval harness trustworthy enough to make a ship/no-ship call within a short window, especially where the target is subjective taste rather than a scored benchmark
You'll structure and generate datasets, including synthetic data pipelines, from whatever the customer actually has
You'll distill specialist models down into smaller models
You'll drive the time-to-model, which means finding what's actually on the critical path and removing it, run after run
You'll embed with customers as a forward-deployed researcher, then hand the pipeline over cleanly when their team is ready to take over
Someone who's shipped post-trained models into production and can talk honestly about the tradeoffs
Someone with hands-on depth across SFT and RL (DPO, GRPO, PPO or similar)
Someone who can judge evaluation honestly: what to measure, what a result means and when a number is lying to you
Someone who's comfortable owning data: curation, filtering, labeling workflows and synthetic generation
Someone proficient in PyTorch or JAX
Someone willing to sit with customers' domain experts to turn their intuition into a reward function
Someone who's worked on RL infrastructure at scale: rollout engines, distributed training or throughput debugging
Someone with experience in distillation, quantization and speculative decoding
Someone who's post-trained for agents and tool use
Someone who's been forward-deployed or has customer-facing engineering experience
Most post-training people spend most of their time fighting infrastructure that they don't control and your ideas are never the bottleneck, the ability to execute them is. We invest heavily into integrating AI tooling into our infrastructure and model foundry to speed up the research process and get you from idea → result as fast as possible. Beyond that, the people who wrote the rollout engine, the inference engine and the kernels under both are the same people you eat lunch with, so if your run is slow, a fix is a conversation and not a ticket.
We're a small team operating at frontier scale. We pre-trained foundation models on 4,000 AMD GPUs as a team of three, designed and brought up GB300 NVL72 clusters and designed a TOP500 supercomputer.
We believe that hard problems get solved in person and most of our work happens at our office in San Francisco. We offer relocation assistance and, where possible, we'd like you here as often as possible.
The base salary range for this full-time position is $275,000-$315,000, plus meaningful equity and benefits.