Diffusion AI Staff Scientist — Equity Options

RadixArk; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

RadixArk is seeking a Member of Technical Staff — Diffusion Model to push the frontier of generative AI. You will design and implement next‑gen diffusion and flow‑based models for image, video, and multimodal generation, balancing research novelty with production readiness.

This role blends deep theoretical work with scalable engineering: training large models on GPUs/TPUs, optimizing sampling and speed, and deploying systems at scale.

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.
• Ability to move from research prototypes to production‑quality models.

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

Diffusion Models

Deep Learning

GPU/TPU Training

PyTorch/JAX

Production Deployment

Probability & Stats

Optimization

Tools

PyTorch

JAX

Job description

RadixArk is seeking a Member of Technical Staff — Diffusion Model to push the frontier of generative AI. You will design and implement next‑gen diffusion and flow‑based models for image, video, and multimodal generation, balancing research novelty with production readiness.

This role blends deep theoretical work with scalable engineering: training large models on GPUs/TPUs, optimizing sampling and speed, and deploying systems at scale.