Post-Doctoral Researcher
Post-doctoral researcher conducting independent and collaborative AI/ML research focused on high-impact domains like medicine, finance, and law. Requires a recent or imminent PhD and publications in top venues.
Research Scientist – Tabular & Structured Machine Learning
Conduct research and build foundational ML models for structured and tabular data, combining statistical learning theory, probabilistic modeling, and large-scale systems. Requires a PhD and strong experience in tabular/relational ML.
160k – 250kSan Francisco, CAAI ResearchOnsiteEntry level
About the role
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
Skills
Machine LearningPyTorchJAXTensorFlowPythonRustTabular Machine LearningRepresentation LearningProbabilistic ModelingStatistical Learning Theory
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