Member of Technical Staff — Diffusion Model

Responsibilities

• Design and develop next-generation diffusion and generative models
• Improve model quality, controllability, and sample efficiency
• Research and implement novel training and sampling methods
• Optimize models for large-scale distributed training
• Collaborate with systems teams to scale training and inference
• Translate research ideas into practical production systems
• Evaluate models using rigorous metrics and benchmarks
• Contribute to long-term research and product direction in generative AI

Requirements

• 5+ years of experience in ML research or applied ML engineering
• 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
• Strong proficiency in PyTorch or JAX
• Experience implementing research ideas into working systems
• Strong mathematical foundation in probability, statistics, and optimization
• Ability to move from research prototypes to production-quality models

Preferred Qualifications

• Publications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR, etc.)
• Experience with large-scale distributed training
• Experience in multimodal generation (text-to-image, video, audio)
• Familiarity with transformer architectures and hybrid models
• Experience improving sampling speed and generation efficiency
• Contributions to open-source generative model projects
• Experience scaling models to billions of parameters

About the Company

RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang, and developed Miles. We're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training.