Staff Engineer, Generative Diffusion & Multimodal AI

RadixArk; Palo Alto (CA); On-site; USD 200,000 - 400,000; Full time

Job summary

RadixArk is seeking a Member of Technical Staff — Inference-Multimodal & Diffusion to drive the frontier of generative modeling. You will work on diffusion and flow-based models for image, video, and multimodal generation, pushing quality, efficiency, and scalability.

You will blend deep research with engineering execution—designing novel algorithms, training at scale, and deploying models for researchers, developers, and real-world applications.

Qualifications

• 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.

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.

Skills

ML research/Applied ML engineering

Diffusion models

Deep learning fundamentals

GPUs/TPUs training

PyTorch or JAX

Research-to-production

Probability and optimization

Tools

PyTorch

JAX

Job description

RadixArk is seeking a Member of Technical Staff — Inference-Multimodal & Diffusion to drive the frontier of generative modeling. You will work on diffusion and flow-based models for image, video, and multimodal generation, pushing quality, efficiency, and scalability.

You will blend deep research with engineering execution—designing novel algorithms, training at scale, and deploying models for researchers, developers, and real-world applications.