ML Researcher - Image / Video Diffusion
Quick facts
- Type
- Full-time
- Location
- San Francisco, California
- Skills
- PyTorch
- Industry
- Information Technology — 671 open jobs
- Visa in listing
- H-1B, OPT
- Apply
- Free to apply — never pay anyone for a job offer or visa sponsorship
Job description
About Krea
At Krea, we are building next-generation AI creative tools.
We're dedicated to making AI intuitive and controllable for creatives - our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats - text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. We recently took this a step forward with the launch of Krea 2, our first foundation model, built completely from scratch for aesthetic diversity and stylistic control.
We've raised over $83M and are backed by world-class investors such as a16z, Bain Capital, and Abstract. We work full-time and in-person at our waterfront office in San Francisco. We care about creativity: our team includes musicians, designers, visual artists, and engineers.
We're looking for an experienced Researcher with engineering skills who can work on large-scale image and video models training experiments, with experience training image models at scale.
Our culture
- We work full-time and in-person at our North Beach office in San Francisco.
- We believe that demonstrated interest in the creative space is key: our team includes musicians, designers, visual artists and more.
- Fast iteration and execution speed. Bias towards action, agency, and independence.
What you'll do
- Train diffusion models for image and video generation on large GPU clusters.
- Fully optimize and profile large distributed training runs across model architectures, kernels, data loading, memory constraints, and communication.
- Implement and improve various distributed training strategies including FSDP, CP, SP, TP, and EP.
- Continuously improve model quality and reliability through data, model architecture, training pipeline, structuring experiments, and eval design.
- Debug distributed training errors and implement fault tolerance solutions, identifying bad GPU, NVLink, Infiniband (IB) components as well as monitoring numerical errors and NCCL issues.
- Ablate different architecture, attention, optimizer, data, and algorithmic choices to reliably improve efficiency and performance of our models.
What we're looking for
- Proven track record in working with image or video models at scale (publications or open-source contributions a plus).
- Strong proficiency in PyTorch and understanding of its inner workings.
- Strong background in distributed training paradigms such as FSDP, CP, SP, USP, TP, and EP. Knowing how different parallelism strategies work together and their tradeoffs.
- Experience in profiling and debugging large distributed training. Being comfortable with analyzing traces to identify bottlenecks and look for improvements.
- Good knowledge of low precision training / inference in FP8, NVFP4, and MXFP8.
- Solid understanding of diffusion model training pipeline across pretraining, midtraining, preference optimization, and reinforcement learning.
- Keeping up with the developments in related fields such as LLM, VLM, representation learning, and robotics research.
- Being comfortable working in a goal-oriented research environment.
- Having good judgement around when one should explore different training strategies and when it's time to commit to a specific strategy to scale compute and data.
- Comfortable working with underspecified goals. We expect every technical member to take an ambiguous research goal and break it down into concrete requirements, plans, experiment plan, and execution items.
- Good research taste — bias towards simplicity and methods that scale well with compute, data, and minimal human supervision.
- Ability to iterate rapidly, and propose creative research directions.
- Be comfortable getting your hands dirty with data and designing custom data pipelines to improve data quality.
What we offer
- Team: Work alongside a world-class team building the future of AI creative tooling
- Impact: Significant scope and company-wide impact
- Competitive compensation: generous salary & equity packages
- Health & wellness: 100% health & 99% dental/vision insurance premiums covered for employees, health FSA accounts, & long-term disability coverage
- Time off: Flexible PTO policy
- Financial planning: 401k with a 4% company-sponsored match
- Meals in the office: breakfast, lunch, dinner - you name it, we'll cover it
- Transit: Ubers covered to & from the office
- Sponsorship: We're open to sponsoring international visas where we can (e.g., STEM OPT, OPT, H-1B, O-1, E-3).
- And more!
Please note the above benefits & perks are for full-time employees
Visa sponsorship record
No public sponsorship records foundWe didn't find H-1B, H-1B1, E-3 or green card (PERM) filings under the name Krea in the Department of Labor and USCIS data. That doesn't mean the job can't be sponsored — the company may file under a different legal name, be new to sponsorship, or sponsor a visa that isn't in these datasets (for example H-2B or J-1).
Tip: ask the recruiter early, in writing, which visa they sponsor and whether they cover the legal and filing fees.
Which visas can work for this job
Occupation: Data Scientists (SOC 15-2051) — estimated from the job title.
- Mentioned in the listing
H-1B, OPT
- H-1B cap-exempt employer
Regular cap-subject employer — a new H-1B needs to win the lottery in March (unless you already hold cap-counted H-1B status).
- TN (citizens of Canada and Mexico)
This kind of role may fit the USMCA profession “Mathematician / Statistician” — it depends on the actual duties. No lottery, no cap; you need the matching degree or license.
- E-3 (Australia) and H-1B1 (Chile, Singapore)
Same degree requirement as H-1B, but no lottery and a separate quota that is rarely filled.
- O-1 (extraordinary ability)
For candidates with awards, publications, press, a high salary or critical roles at distinguished organizations. No cap, no lottery; the employer files a petition.
- STEM OPT extension (F-1 students)
Requires an E-Verify employer. We didn't find this company in the E-Verify list — ask HR.
Salary vs prevailing wage
Level II (qualified)Data Scientists · San Francisco-Oakland-Fremont, CA. Annual prevailing wages set by the Department of Labor (OFLC).
| Level | Prevailing wage | H-1B lottery odds* |
|---|---|---|
| Level I | $110,386 | ~15% |
| Level II | $142,438 | ~31% |
| Level III | $174,512 | ~46% |
| Level IV | $206,565 | ~61% |
This job pays $172,640 a year (converted from an hourly rate, 2,080 hours) — that's Level II (qualified). In the wage-weighted H-1B lottery a Level II registration gets 2 entries; estimated selection chance about 31%.
* Odds are DHS projections for the FY2027 wage-weighted lottery (actual results vary by year and employer). Since the FY2027 cap season the H-1B lottery is weighted by wage level: Level I = 1 entry, II = 2, III = 3, IV = 4. The level is set by the offered wage against the prevailing wage for the occupation and worksite. Separately, a $100,000 fee for new H-1B petitions for workers outside the US was announced in 2025; as of September 2026 a federal court ruling keeps it unenforceable while appeals continue — check the current status.
Sources: U.S. Department of Labor OFLC disclosure data (H-1B/H-1B1/E-3 LCA, PERM), OFLC prevailing wage data, USCIS H-1B Employer Data Hub, E-Verify participating employers. Data loaded: LCA FY2024–FY2026, PERM, USCIS Data Hub, OFLC wages; updated 2026-10-03. Employers are matched by name, so records of companies with similar names can occasionally be mixed up. This is general information, not legal advice — talk to an immigration attorney about your case.