Research Scientist – Tabular & Structured Machine Learning

What You’ll Build and Research

• Invent and prototype algorithms that advance the foundations of machine learning for structured and tabular data
• Develop new representation learning techniques and information models for large enterprise datasets
• Build adaptive learners combining statistical learning theory, probabilistic modeling, and large-scale systems optimization
• Contribute to the development of large tabular models and structured foundation models
• Design architectures integrating relational, symbolic, and neural learning components
• Research and implement methods for dataset compression, selection, and representation to improve learning efficiency
• Develop cost models and optimization frameworks for large-scale structured learning systems
• Collaborate closely with the Granica research group led by Prof. Andrea Montanari (Stanford) and with systems engineers
• Rapidly prototype new algorithms and evaluate them on real enterprise datasets
• Publish and contribute to the broader research community shaping the future of structured AI and efficient ML systems

What You’ll Bring

• PhD in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related field
• Research experience related to structured, relational, or tabular data
• Experience in one or more of the following areas:
  • Tabular or relational machine learning
  • Representation learning for structured data
  • Statistical learning theory or generalization
  • Probabilistic modeling or Bayesian inference
  • Optimization for machine learning
  • Scalable or distributed ML systems
• Experience working with structured datasets or relational data systems
• Strong grounding in statistics, optimization, information theory, or probabilistic inference
• Hands-on experience with PyTorch, JAX, or TensorFlow
• Strong programming skills in Python or Rust
• Demonstrated ability to translate theoretical ideas into working systems or prototypes
• Curiosity about how structure and relational information enable new forms of learning and reasoning
• A pragmatic research mindset: you value elegant ideas but also ship systems that work at scale

Bonus

• Research in tabular machine learning, relational representation learning, or structured data modeling
• Experience building large-scale ML infrastructure or distributed training systems
• Familiarity with data systems, query engines, or dataset optimization pipelines
• Publications at top venues such as NeurIPS, ICML, ICLR, COLT, KDD, AAAI
• Contributions to open-source ML systems or research-to-production tooling

Compensation & Benefits

• Competitive salary, meaningful equity, and substantial bonus for top performers
• Flexible time off plus comprehensive health coverage for you and your family
• Support for research, publication, and deep technical exploration