Senior Deep Learning Scientist, Multimodal Agentic RL

Responsibilities

• Apply fundamental and applied research to develop, train, fine-tune, and deploy large language models for agentic systems encompassing audio-visual reasoning, tool usage, and document understanding.
• Advance post-training and alignment methods including instruction tuning, preference optimization, and RLHF/RLVR/MOPD to improve multimodal agents for complex use cases.
• Research and develop agentic reasoning and grounded perception capabilities, focusing on planning, tool execution, and long-horizon task completion across digital and physical environments.
• Lead the collection, development, and benchmarking of multimodal datasets, ensuring high-quality evaluation of model accuracy, safety, and task completion success.
• What we need to see
• Master’s degree (or equivalent experience) or PhD in Computer Science, AI, or Applied Math with 5+ years of relevant work experience.
• Excellent programming skills in Python with strong fundamentals in scalable model development and deep learning frameworks like PyTorch.
• Strong knowledge of ML/DL techniques and modern foundation model architectures, including Transformers and mixture-of-experts models.
• Foundational understanding of reinforcement learning algorithms and implementation, including MDPs, policies, and reward design.
• Hands-on experience in post-training multimodal models for omni-modality (audio-visual) reasoning, full-duplex voice chat, and human-AI interaction.

Languages