CATCHES
AI Engineer (Image & Video)
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CATCHES builds physics-backed AI for garment simulation and virtual try-on, used by luxury fashion brands. Launched at Nvidia GTC 2026 after several years in stealth at the cutting edge of physics-informed research and development, we are now handling enterprise scale, consumer-facing virtual try-on solutions for global brand partners.
Backed by visionary investors—including Antoine Arnault, Natalia Vodianova Arnault, Roy Chung (Apollo.io), Dillon Erb (Paperspace), Gary Sheinbaum (formerly CEO of Tommy Hilfiger), and Sarah Willersdorf (BCG)—we are scaling with a unique fusion of luxury fashion expertise and ** cutting-edge AI**.
Location: Remote (based in the UK/Europe/USA)
The Role: AI Model Scientist (Applied Physics + Diffusion Models)
We're building a globally visible tool used by consumers and luxury brands. As AI Model Scientist, your focus will be improving high-stakes visual outputs — this is a role for someone who loves making real-time visible quality contributions at scale.
Key Responsibilities:
- Own end-to-end model improvements by deeply collaborating with cross-functional image/vision technology teams.
- Refine and optimise cutting-edge diffusion models for loops, pose consistency, artefact suppression and brand-critical outputs.
- Adapt and optimise physical/numerical simulations (e.g., cloth simulation AI) via our custom Python tooling and ML pipelines.
- Work with large setups, validate pipelines, and establish best practices to produce photorealistic, market-ready imagery.
- Metric-driven mentality – balancing statistical rigor with real-world artistic needs.
- Fine tuning, hyperparameter explorations, systemic model distillation, and cloud-based optimisations.
- Distilling domain knowledge from fashion brands and sharing practical insights with our product teams.
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Requirements
Must Haves:
- Hands-on experience with diffusion-based image and/or video generation models (e.g., Diffusion Transformer-based models, Stable Diffusion XL, Imagen, or open-source alternatives).
- Production experience in deploying, optimising, and tuning vision-language models (VLM) for performance, quality, and consistency.
- Expertise in fine-tuning/lofting foundation models to fit specific use cases — excel at adapting models to solve novel visual tasks in fashion, retail, and (or) virtual try-on domains.
- Solid Python proficiency for MLOps and model-serving tasks (including workflows on GPU clusters).
- Metrics-driven approach to evaluating visual output — comfortable with quantitative A/B comparisons.
- familiarity with numerical simulation and/or numerical optimisation techniques.
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Nice to Have:
- Flux (BlackForest/Lab) and/or LTX application experience would be a game-changer.
- Experience rewriting architecture and decoding temporal trajectories for controlled outputs.
- Hands-on experience with ControlNet/IP-Adapter or texture-conditioning approaches.
- Experience with video generation/gated autoregressive architectures for temporal consistency.
- In-depth knowledge of fashion/garment/retail (UX, VR) visual generation challenges.
- Expertise with cloud GPU inference and optimised workloads (GCP, AWS, or the like, backed by existing SolveLabs/Ollama-style stacks).
Note: This is a role for someone who loves the messy— not the glue, but the scariest asteroid of optimization.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
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