Research Scientist / Engineer — Multimodal Agent
Builds and trains large-scale multimodal agentic models involving reasoning, planning, coding, and tool calling. Requires strong ML foundations, PyTorch expertise, and experience with distributed training on massive datasets.
What You'll Do
Modeling
- Architect large-scale multimodal agentic models that use reasoning, planning, coding, and tool calling to achieve complex, multi-step multimodal work.
Data
- Hillclimbing existing tasks and formulating new tasks through data.
- Design, implement, and run robust data pipelines for constructing, enriching, and filtering massive pixel datasets.
Systems
- Train large-scale multimodal models on massive datasets and GPU clusters.
Evaluation
- Define and build novel evaluation frameworks to measure multimodal agents.
Who You Are
- Strong foundation in machine learning, foundation models and agentic systems.
- Deep understanding of agentic systems and approaches in LLM/VLM reasoning, coding models, LLM/VLM tool calling.
- Hands-on experience with PyTorch and large-scale training (distributed, mixed precision, large datasets).
What Sets You Apart (Bonus Points)
- Experience in the following around data, modeling, or evaluation: State-of-the-art foundation models in reasoning, State-of-the-art foundation models in coding, State-of-the-art foundation models in tool calling, State-of-the-art multimodal agents.
Compensation
- The base pay range for this role is $250,000 – $450,000 per year.
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