GenPeach AI is a product-driven research lab building vertical multimodal foundation models for hyper-realistic human generation in image and video – designed for emotionally resonant, human-centered AI experiences. Our goal is to create tools that supercharge human creativity rather than replace it.
We train models from scratch: proprietary datasets at massive scale, novel architectures and training recipes, large GPU clusters, and tight product integration so research ships to users quickly.
We are a deeply technical team of around 10 people. We’re advised by Directors from Google DeepMind and backed by leading AI-focused funds and angels from OpenAI, Meta AI, Microsoft AI, Project Prometheus, and Fal. Collectively, our team, advisors, and angels have contributed to models including Meta’s Imagine/MovieGen and foundation-model work behind OpenAI’s Sora, plus Google’s Veo and Gemini.
We’re looking for an exceptional AI Research Scientist to help lead the next generation of GenPeach AI foundation models. This is a Leadership IC role: you’ll shape research direction through hands-on experimentation, guide technical decisions across the stack, and mentor a small group of researchers while working closely with the founders.
This isn’t “fine-tuning an open-source checkpoint.” It’s building new capabilities through architecture work, training systems, and post-training recipes—then deploying them into production.
Co-design and train large-scale diffusion models for image and video generation
Build and iterate on training recipes (pretraining, post-training, control, preference/tuning where relevant) to unlock new model capabilities
Run rigorous ablations: isolate what works, why it works, and communicate outcomes clearly to drive roadmap decisions
Reason about speed/quality/cost tradeoffs and make technical choices that materially affect training efficiency and production quality
Influence and contribute to dataset strategy: curation signals, filtering, evaluations, and feedback loops from product
Collaborate with engineering/product to productionize research (serving constraints, stability, monitoring, fast iteration)
Mentor and raise the bar for a small team of researchers through code, reviews, and research hygiene
Have 5+ years of deep learning / applied AI research experience (or equivalent research impact)
Are strong in Python and PyTorch, and comfortable owning research code that becomes production-critical
Have hands-on experience training and debugging foundation models (not “black-box use”): you’ve dealt with instability, collapse, data issues, scaling pathologies, and know how to fix them
Can move fast with good taste: you prioritize the experiments that matter and make decisions with incomplete information
Communicate research clearly – through writing, plots, ablations, and crisp takeaways
Take ownership beyond your job description when needed (startup reality)
5+ years of experience in AI research / deep learning (industry or academia)
Excellent grasp of modern generative modeling (e.g., diffusion/flow, GANs, VLMs or adjacent modalities)
Strong software skills: Python + PyTorch, solid engineering hygiene for experimentation and reproducibility
Proven ability to drive projects end-to-end with high autonomy
Experience training large diffusion/flow models for image/video, or adjacent large-scale generative work (LLM/VLM/speech) with transferable scaling and post-training expertise
Experience training at scale (multi-node, hundreds of GPUs), and understanding distributed training failure modes
Experience designing model architectures or core training components (losses, conditioners, schedulers, sampling, distillation, etc.)
Publications at top venues (NeurIPS/ICML/ICLR/CVPR/ICCV/ECCV/ACL) or equivalent research impact
Experience leading technically (as an IC): mentoring, setting research direction, improving team execution
Build frontier image/video models end-to-end: data architecture training post-training production
High ownership and fast iteration in a lean team—your work directly shapes what we ship
Collaboration across research and engineering with minimal process overhead
A chance to compete on the global stage of foundation model quality—and ship results publicly
High ownership and accountability
Direct, low-ego communication
Bias toward impact: measure iterate ship
Strong technical standards and research discipline
Location: Zurich (Switzerland) or Warsaw (Poland) — onsite or hybrid. If you’re elsewhere, we’re open to remote (team/timezone fit considered).
Compensation: competitive salary + meaningful equity (level-dependent)
Interview process: quick screen research round technical round (practical + systems) team fit/values
Visa sponsorship (where applicable); we’ll make a strong effort to relocate you to Switzerland or Poland if desired
Remote-friendly: work fully remote, hybrid, or on-site from our hubs
Regular offsites and in-person events to collaborate and connect
Flexible PTO
