ML Researcher - Posttraining

Skills

About the Role

You will fine-tune diffusion models at scale and implement post-training techniques including supervised fine-tuning, preference optimization, reinforcement learning, and distillation. You will develop evaluation and reward systems, train VLM and LLM models, coordinate preference-data collection, and work on safety alignment.

Requirements

• Experience post-training diffusion models for image or video generation
• Experience with large-scale model training, inference, and optimization
• Knowledge of LLM and diffusion post-training pipelines
• PPO, GRPO, DPO, OPD, and MOPD
• PyTorch
• Distributed training including FSDP, CP, SP, USP, TP, and EP
• Low-precision training and inference in FP8, NVFP4, and MXFP8
• vLLM and sglang
• Reinforcement learning infrastructure and optimization
• Experience training VLM models
• Knowledge of LLM, VLM, representation learning, and robotics research

Responsibilities

• Fine-tune diffusion models at scale to improve image aesthetics and quality
• Implement supervised fine-tuning, preference optimization, reinforcement learning, distillation, and acceleration techniques
• Design evaluation suites and reward systems for reinforcement learning
• Train VLM reward models
• Train LLMs for prompt expansion
• Coordinate preference-data and model-evaluation collection
• Work on safety alignment for open-source release
• Integrate research advancements into products

Benefits

• Equity packages
• Health insurance
• Dental and vision insurance
• Health FSA accounts
• Long-term disability coverage
• Flexible PTO
• 401k with a 4% company-sponsored match
• Office meals
• Uber transit to and from the office
• Visa sponsorship