# Founding Machine Learning - World Models at One Robot

> Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aonerobot%3A084b67a9-db33-433b-bd81-e131a359b0a7 (read 2026-11-07).

## Facts (observed)

- Company: [One Robot](https://alderhq.app/companies/one-robot)
- Location text: San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco")
- Posted: 2026-08-05 (94 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/onerobot/084b67a9-db33-433b-bd81-e131a359b0a7
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: $150k–$275k per year (from the listing's pay field)
- Market band for similar posted roles (SF Bay Area, n 461): p25 $200k, median $250k, p75 $300k

## Description (the employer's text)

We build world models that simulate manipulation scenes faithfully enough to validate, and one day, train policies without touching a robot. You'll develop generative models that make this work, with the controllability and physical fidelity to match real-robot behavior.

### What you'll do

Train video and dynamics models: Develop world models with action conditioning for manipulation policies.

Push long-horizon coherence: Develop architectures and training methods that extend rollout quality on hard physical tasks.

Own training infrastructure: Run multi-GPU clusters, write custom CUDA, debug at scale.

Build the world-model data engine: Design, implement, and improve a data engine that allows the world model to compound learning across customers and manipulation tasks.

### Requirements

Very strong coding in Python and PyTorch (or similar).

Video generation experience: Deep experience training image or video generation models end-to-end.

Large-scale training: Track record operating training runs at cluster scale.

3D vision: Working knowledge of multi-view geometry, scene reconstruction, and physical priors.

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aonerobot%3A084b67a9-db33-433b-bd81-e131a359b0a7 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.