Research Scientist Intern, VLM and Omni Model Post-Training (PhD)

What you'll do

• Develop research, training pipelines, reward functions, experiments, and prototype systems for multimodal post-training.
• Benchmark reinforcement-learning methods against supervised fine-tuning and preference-optimization baselines; evaluate behavior and generalization.
• Publish and present research findings to internal and external audiences.

What they ask for

• Currently has or is pursuing a PhD in machine learning, computer vision, natural language processing, speech and audio processing, reinforcement learning, or a related field.
• Research contributions demonstrated through grants, fellowships, patents, or publications at leading workshops or conferences.
• Knowledge of deep learning and multimodal foundation models, with hands-on VLM or omni model post-training experience, particularly reinforcement learning.
• Experience implementing deep-learning algorithms, training models, designing evaluations, and programming in Python with frameworks such as PyTorch.

Nice to have

• Experience with distributed training, rollout generation, and scalable evaluation of multimodal foundation models.
• Research experience in video understanding, temporal reasoning, or audio-visual learning.

Pay

$7,650/month to $12,667/month (base salary)

Tools

Python, PyTorch

AI focus

Develop reinforcement-learning post-training methods, reward models, and evaluations for multimodal reasoning, temporal grounding, calibrated decisions, and grounded decision-making in vision-language and omni models.