Research Scientist, Foundation Model

What you'll do

• Identify research questions, design experiments, and interpret results.
• Implement, train, and analyze models and learning methods that generalize across datasets and tasks.
• Collaborate with research engineers to develop promising ideas into reliable models.

What they ask for

• Track record of original research contributions, such as publications at leading venues, influential open-source work, benchmarks, or deployed methods; exceptional earlier-career work is also welcomed.
• Strong machine-learning model development and analysis experience, including implementing, training, and debugging methods in PyTorch.
• Solid understanding of training dynamics, generalization, and deep-learning failure modes; strong experimental judgment.
• Excellent engineering fundamentals and Python skills, with a record of writing high-quality research code.

Nice to have

• Research experience in tabular data, time series, relational learning, causal inference, or recommender systems.
• Experience with scaling, distillation, inference optimization, efficient architectures, open-source ML contributions, or an early-stage startup or research lab.

Tools

PyTorch, Python

AI focus

Research and develop foundation models for structured data, potentially covering tabular data, time series, forecasting, relational learning, causal inference, recommender systems, and multimodal modeling.