Member of Technical Staff — Inference-Multimodal & Diffusion
RadixArk
Salary undisclosed
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Description
About the Role
RadixArk is seeking a Member of Technical Staff — Inference-Multimodal & Diffusion to advance the frontier of generative modeling.
You will work on cutting-edge diffusion and flow-based models for image, video, and multimodal generation, pushing model quality, efficiency, and scalability. This role combines 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.
This is a high-impact role for engineers and researchers who want to push the limits of generative models in both theory and practice.
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
Strong Plus
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
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
About RadixArk
RadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, we're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training. Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.
Compensation
Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.
Equal Opportunity
RadixArk is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
About this role
RadixArk is building infrastructure for frontier AI and is hiring a Member of Technical Staff to advance generative modeling at scale. You'll work on diffusion and flow-based models for image, video, and multimodal generation, balancing research innovation with production engineering. The role spans algorithm design, large-scale GPU/TPU training, and deployment—moving from research prototypes to systems that researchers and developers actually use.
This position requires at least five years in ML research or applied engineering, with deep expertise in diffusion models and generative systems. You'll need strong fundamentals in deep learning and optimization, hands-on proficiency in PyTorch or JAX, and proven ability to train large models and translate research into working code. A solid mathematical foundation in probability and statistics is essential, as is the ability to move fluidly between exploration and production-quality implementation.
The role suits researchers or engineers who've published at top venues, worked on multimodal generation, or scaled models to billions of parameters—though these are valued additions, not strict requirements. You'll collaborate with systems teams, evaluate models rigorously, and help shape RadixArk's long-term direction in generative AI. The position is based in Palo Alto and requires in-office presence. Compensation ranges from $200,000 to $400,000 annually plus equity, depending on background and experience.
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