Member of Technical Staff, Post-Training & Applied Research

SF Tensor Company; San Francisco (CA); Sur place; USD 275 000 - 315 000; Plein temps

Résumé du poste

SF Tensor is building the fastest GPU compiler and a high-performance enterprise modeling platform in San Francisco. This role centers on owning the post-training pipeline end-to-end—from data curation through SFT, RL, distillation and deployment.

You'll work with customers’ domain experts to craft reward functions, design evaluation frameworks and build an eval harness that supports ship/no-ship decisions in a tight window.

Qualifications

• Experience shipping post-trained models into production.
• Hands-on depth with SFT and RL (DPO, GRPO, PPO or similar).
• Ability to design evaluation metrics and interpret results honestly.
• Experience managing data curation, labeling workflows and synthetic data generation.

Responsabilités

• Own the post-training pipeline end-to-end including data curation, SFT, RL, evals and distillation.
• Design reward functions and RL objectives with customer domain experts.
• Build an eval harness capable of ship/no-ship decisions within tight windows.
• Structure datasets including synthetic data pipelines from customer data.
• Distill specialist models into smaller, deployable versions.
• Identify critical path bottlenecks and optimize iteratively.
• Collaborate with customers as a forward-deployed researcher and transfer ownership.

Connaissances

Post-training ML

Reinforcement learning

Evaluation design

Data curation

Outils

PyTorch

JAX

Description du poste

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 benchmarkacross 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 datasetinto 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 anydetail all in a versioned and reproducible manner, making it trivial to reuse and modify recipes. Underneath that is the most powerful engineyou could ask for, which has been used to post-train multi-100B language parameter models on TPU, post-train robotics models on Trainiumand 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 tasterather 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 theresearch process and get you from idea result as fast as possible. Beyond that, the people who wrote the rollout engine, the inference engineand 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 andbrought 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 relocationassistance 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.