Founding Machine Learning - World Models
San Francisco · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
One Robot Founding Engineers — team source
Department: One Robot Founding Engineers
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require visa sponsorship? (Yes/No)
From the employer’s application form
What you’ll work on
Full postingTrain action-conditioned video and dynamics world models for manipulation policies
Develop architectures and training methods that keep rollouts coherent on long-horizon physical tasks
Run and debug multi-GPU training infrastructure, including custom CUDA
Design and build a data engine so the world model learns across customers and tasks
From the employer’s posting
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.
What you’ll bring
All qualificationsCore experience
- Very strong coding in Python and PyTorch (or similar)
- Deep experience training image or video generation models end-to-end
- Track record operating training runs at cluster scale
- Working knowledge of multi-view geometry, scene reconstruction, and physical priors
Qualification wording
Very strong coding in Python and PyTorch (or similar).
Deep experience training image or video generation models end-to-end.
Track record operating training runs at cluster scale.
Working knowledge of multi-view geometry, scene reconstruction, and physical priors.
Tools in this posting
- Python
- PyTorch
Source — Tool mentions in context
Requirements: - Very strong coding in Python and PyTorch (or similar). - Video generation experience: Deep experience training image or video generation models end-to-end.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 12, 2026 · 78 days ago
- Recorded sightings
- 37
- Last seen by us
- Oct 28, 2026
- Employer says posted
- Aug 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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Job description
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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