AI Research Scientist​/Engineer

Job Overview

Seeking top-tier PhDs (Bay Area preferred) with ICML/ICLR publications in LLM training and inference optimization—no vision/audio, just pure language; diffusion model experience a plus.

Client Opportunity | Through Phizenix (WBENC & Minority-Certified Recruiting Partner)

Join a trailblazing AI startup that’s reinventing how large language models are built—with diffusion-powered LLMs that generate faster, adapt smarter, and handle multimodal data like no other.

We’re looking for a Research Scientist / Engineer who’s ready to move beyond traditional autoregressive methods and help shape the next wave of generative AI. You’ll collaborate with pioneers in AI research, design novel model architectures, and scale your ideas from paper to production.

• Design and refine LLM architectures built on a diffusion-first paradigm.
• Develop cutting‑edge training strategies and custom loss functions.
• Translate research into real‑world systems for enterprise‑scale deployments.
• Explore constraint‑aware generation and controlled outputs.
• Push the limits of model efficiency, scalability, and multi‑modal capabilities.

• PhD in Computer Science, Machine Learning, or a related field.
• Hands‑on experience with PyTorch and LLM fundamentals (transformers, KV caching, etc.).
• Recent or any ICLR/ICML publications in LLM inference optimization (ideal).
• Familiarity with diffusion models and distributed model training.
• Solid research‑to‑production mindset with 2+ years in an ML/AI role.

• Training LLMs from scratch and optimizing large‑scale runs.
• Advanced training tactics (e.g., mixed precision, gradient accumulation).
• Experience with cross‑modal modeling and inference frameworks like vLLM, Tensor
  
  RT.
• Background in model efficiency, optimization theory, or infrastructure‑aware research.

$180,000 - $200,000 USD

As set forth in Phizenix’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.