ABOUT THE COMPANY

We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small, well-funded team that works onsite in San Francisco.

ABOUT THE ROLE

You'll lead our work on model post-training: supervised fine-tuning, preference data, reinforcement learning from human and AI feedback, reward modeling, and the evaluation suites that tell us what's actually working. You'll own a research area that meaningfully shapes our model behavior and capability.

This is a hands-on senior research role. You'll set direction, run experiments, and ship into production. You'll partner with the data, infrastructure, and engineering teams to make the post-training pipeline reliable and fast: improvements there compound into every model we ship.

WHAT YOU'LL DO

Lead post-training research: SFT, RLHF/RLAIF, RLVR, DPO and successor methods, reward modeling, preference data design

Design and curate the data that goes into post-training (from sourcing, to filtering, to quality assessment)

Build and maintain the evaluation suites that measure what matters; resist Goodharting your own benchmarks

Run rigorous experiments (controls, ablations, statistical significance) and write up internal findings clearly

Scale data pipelines and the infrastructure team to scale training

Identify and characterize failure modes (reward hacking, distribution drift, eval saturation) and design experiments to address them

Stay current on the post-training literature; bring useful methods in, ignore the noise

WHAT WE'RE LOOKING FOR

Strong track record of post-training research (SFT, RL, reward modeling) at a frontier-model lab or equivalent

5+ years of hands-on ML research experience

Comfort with large-scale data curation and preference-data pipelines

Experience designing evaluation suites for capabilities that aren't easily benchmarked

Fluent in PyTorch or equivalent; comfortable at the scale of distributed training

Strong statistical instincts: you'd notice a flawed comparison before someone else points it out

Strong written communication

NICE TO HAVE

PhD in ML, statistics, CS, or adjacent

Published research at NeurIPS, ICML, ICLR, COLM, RLC, or comparable venues

Experience with reward hacking detection, scaling reward models, or RLHF infrastructure

Synthetic data generation experience

Background in RL math (policy gradients, importance sampling, off-policy methods)

Open-source contributions to post-training infrastructure

THIS ROLE IS PROBABLY NOT FOR YOU IF

You're primarily interested in pretraining (that's a different role)- You'd rather invent novel methods in isolation than ship them into a model that real users run

You prefer benchmarks that are stable to evaluation work where the right answer isn't yet defined