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RadixArk is seeking a Member of Technical Staff to advance the frontier of generative modeling, specifically working on cutting-edge diffusion and flow-based models for image, video, and multimodal generation. You will combine deep research thinking with strong engineering execution—from designing novel algorithms to training and deploying models at scale. Your work will directly shape next-generation generative AI systems used by researchers, developers, and real-world applications.
Key responsibilities include designing and developing next-generation diffusion and generative models, improving model quality, controllability, and sample efficiency, researching and implementing novel training and sampling methods, and scaling models to production. You'll move from research prototypes to production-quality systems, working on both theoretical advances and practical deployment challenges.
Required qualifications: 5+ years of experience in ML research or applied ML engineering with strong expertise in diffusion models or generative models (DDPM, DDIM, latent diffusion, flow matching, etc.). Deep understanding of deep learning fundamentals and optimization, proven experience training large-scale models on GPUs/TPUs, and strong proficiency in PyTorch or JAX. You should have experience implementing research ideas into working systems and a strong mathematical foundation in probability, statistics, and optimization.
Desirable qualifications include publications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR), experience with large-scale distributed training, multimodal generation (text-to-image, video, audio), transformer architectures and hybrid models, improving sampling speed and generation efficiency, contributions to open-source generative model projects, and experience scaling models to billions of parameters.
