# Member of Technical Staff – AI Research Engineer (Image/Video Foundation Models) at Genpeach > Remote · Europe · Staff · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Agenpeach%3Ad2fd37d4-3c2f-4a76-bf4c-77f869649f20 (read 2026-11-14). ## Facts (observed) - Company: [Genpeach](https://alderhq.app/companies/genpeach) - Location text: Zurich; Warsaw - Where it can be done from (inferred): Remote · Europe (evidence: "Location: Zurich; Warsaw") - Posted: 2026-03-30 (228 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-08 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/genpeach/d2fd37d4-3c2f-4a76-bf4c-77f869649f20 - Skills listed (inferred): Node.js, Python, PyTorch, LLMs ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) ### About GenPeach AI 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. ### About the Team You’ll join the research team working across image/video generation and multimodal understanding. You’ll work closely with other Research Engineers and Scientists, as well as Founders and help turn research into scalable training runs, strong evaluations, and production-ready systems. ### About the Role We’re hiring an AI Research Engineer to help build and scale GenPeach’s foundation models end-to-end – from implementing new model ideas and training recipes, to owning the parts of the training stack that determine quality and speed, to pushing models through production constraints. This is a hands-on, high-ownership role. You’ll write research-grade code that becomes production-critical. ### In this role, you will Implement and iterate on image/video generative model ideas (architecture, losses, conditioning, sampling, pre-training, distillation, post-training) Own training performance end-to-end (distributed training, throughput, memory, stability, debugging scaling failure modes) Build the experimentation loop (evals, ablations, reproducibility tooling, reporting, decision hygiene) Build and improve VLMs for image/video captioning (data recipes, training strategies, model variants, evaluation) Run high-iteration research: read papers when useful, implement ideas, validate empirically Create captioning pipelines that improve generation training and product quality Partner with inference/product to ship under real constraints (latency, cost, reliability, rollout safety) Build demos and prototypes to showcase capabilities and accelerate iteration ### You might thrive in this role if you Love the craft of experimentation: fast iteration, clear ablations, strong evals, and honest conclusions Enjoy debugging messy real-world training runs (not just clean demos) Can move between research and engineering: write clean code, ship utilities, and improve team velocity Take ownership beyond your job description when needed (startup reality) Communicate clearly and collaborate well in a small, senior team ### Minimum Qualifications Strong Python and PyTorch skills (4+ years of experience) Experience implementing and training deep learning models (generative models, VLMs, LLMs, vision/video, or adjacent) Solid understanding of training dynamics, optimization, and practical debugging Ability to drive projects end-to-end with minimal supervision ### Preferred Qualifications Hands-on experience with diffusion/flow-based image or video generation, or large-scale generative modeling in adjacent domains Experience with distributed training at scale (multi-node) and performance tuning (throughput/memory) Experience building evaluation frameworks (offline metrics + human eval + regression tracking) Strong intuition for data quality and dataset/labeling tradeoffs for training and captioning Publications are a plus, but shipped impact and strong technical evidence matter more ### What makes this role unique Build frontier image/video models and the VLM captioning systems that power them Join a lean, senior team that holds a high engineering + research bar Direct product impact: your training runs become real user-facing capabilities --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Agenpeach%3Ad2fd37d4-3c2f-4a76-bf4c-77f869649f20 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.