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Fundamental is an AI research lab pioneering enterprise decision-making technology. The company has developed NEXUS, a Large Tabular Model (LTM) purpose-built for structured business data, and is backed by $275m in funding from leading investors with Fortune 100 customers.
As an ML Researcher on the Research team, you will contribute to ideation, design, implementation, and evaluation of breakthrough machine learning models deployed in production. Your responsibilities span the entire model lifecycle: achieving high evaluation scores through novel architectures and training methods, establishing evaluation protocols, and implementing efficient training and inference techniques. You will work alongside other ML researchers to ensure model performance across all dimensions.
A critical aspect of this role is solid engineering practice. You will contribute to maintaining research code that enables swift, rapid development and testing of new ideas—both your own and the team's. This is a technical individual contributor role focused on advancing the state of foundation models for enterprise applications.
REQUIREMENTS:
Must Have:
- Strong familiarity with the full research cycle in machine learning
- Strong fundamentals of software engineering
- Strong knowledge of Python and ML frameworks
- Experience with: full lifecycle of AI model development, ML infrastructure frameworks and tools, developing new ML methods/algorithms/models, GPUs/TPUs and distributed training, scaling up models and training regimes
- Knowledge of classical ML methods and algorithms, and deep learning techniques
Nice to Have:
- Expertise in architecture research, distillation (model compression), evaluation, code generation, or LLMs
- Experience with foundational models (e.g., LLMs)
- Published research at AI conferences
- Contributions to open source ML projects
- Experience with tabular data and predictive analytics
- High Kaggle rank
- BSc/MSc/PhD in computer science or machine learning
