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.
About the role
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
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We work full-time and in-person at our North Beach office in San Francisco.
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We believe that demonstrated interest in the creative space is key: our team includes musicians, designers, visual artists and more.
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Fast iteration and execution speed. Bias towards action, agency, and independence.
What you’ll do
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Train diffusion models for image and video generation on large GPU clusters.
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Fully optimize and profile large distributed training runs across model architectures, kernels, data loading, memory constraints, and communication.
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Implement and improve various distributed training strategies including FSDP, CP, SP, TP, and EP.
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Continuously improve model quality and reliability through data, model architecture, training pipeline, structuring experiments, and eval design.
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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.
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Ablate different architecture, attention, optimizer, data, and algorithmic choices to reliably improve efficiency and performance of our models.
What we’re looking for
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Proven track record in working with image or video models at scale (publications or open-source contributions a plus).
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Strong proficiency in PyTorch and understanding of its inner workings.
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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.
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Experience in profiling and debugging large distributed training. Being comfortable with analyzing traces to identify bottlenecks and look for improvements.
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Good knowledge of low precision training / inference in FP8, NVFP4, and MXFP8.
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Solid understanding of diffusion model training pipeline across pretraining, midtraining, preference optimization, and reinforcement learning.
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Keeping up with the developments in related fields such as LLM, VLM, representation learning, and robotics research.
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Being comfortable working in a goal-oriented research environment.
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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.
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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.
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Good research taste — bias towards simplicity and methods that scale well with compute, data, and minimal human supervision.
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Ability to iterate rapidly, and propose creative research directions.
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Be comfortable getting your hands dirty with data and designing custom data pipelines to improve data quality.
What we offer
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Team: Work alongside a world-class team building the future of AI creative tooling