Stability AI is the company behind the Stable Diffusion family — open-weights image, video, and audio generative models. Series B+ with 2024 leadership reset. The interview emphasizes generative-model research engineering, large-scale training, and the unique tradeoffs of an open-weights product company.
Process
Recruiter screen → 60-minute coding (Python with ML fluency expected) → onsite virtual: 2 coding/ML, 1 ML system design, 1 research deep-dive, 1 behavioral. Research candidates get a paper-discussion round. Cycle: 4–6 weeks.
What they actually ask
- Design a distributed training pipeline for a diffusion model
- Design an inference platform with high throughput for image/video generation
- Design a safety-and-watermarking pipeline for outputs
- Coding: medium-hard DSA, often ML-flavored
- Behavioral: ownership, taste, research-engineering blend
Levels and comp (2026)
- SE: $170K–$235K total
- Senior SE: $245K–$340K total
- Staff / ML Research: $360K–$520K+ total at top of band
(Comp tightened post 2024 restructuring; below frontier-lab top of band.)
Prep priorities
- Be fluent in Python and PyTorch deeply
- Understand diffusion models (DDPM, score-based, flow matching, rectified flow)
- Brush up on distributed training (FSDP, DeepSpeed) and inference optimization for diffusion
Frequently Asked Questions
Is Stability remote-friendly?
Hubs in London (HQ) and remote across US/EU. Many engineering and research roles remote.
How does Stability compare to Black Forest Labs (Flux), Midjourney, or Runway?
Black Forest Labs (founded by ex-Stability researchers) ships frontier image quality. Midjourney is a closed product with leading aesthetic quality. Runway is video-first. Stability’s differentiator is open-weights releases. Comp lower than top-tier closed labs but with strong research brand.
What is the engineering culture?
Research-engineering blended; calmer post-2024 reorg. Strong OSS / open-weights ethos. The London + remote distribution requires async discipline.