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.

What You'll Do

• 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

What We're Looking For

• 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

Nice to Have

• 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

Why Join Us

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.

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