Our long-term vision is Efficient Intelligence : AI that learns using fewer resources, generalizes from less data, and reasons through structure rather than scale. To reach that, we are first building the Foundational Data Systems that make structured AI possible.

What You’ll Build and Research

• Invent and prototype algorithms that define the foundations of structured AI , advancing representation learning and efficient information modeling for enterprise and tabular data at petabyte scale.
• Develop adaptive learners that fuse statistical learning theory with large-scale systems optimization, contributing to a new generation of foundational models for structured information .
• Design architectures that integrate symbolic, relational, and neural components, enabling AI systems to reason directly over structured enterprise data.
• Build cost models and optimization frameworks that make structured learning efficient, both computationally and economically.
• Collaborate closely with the company's Research group led by Prof. Andrea Montanari (Stanford) and with systems engineers to transform theoretical ideas into production-grade systems used across live enterprise workloads.
• Iterate fast: prototype new model architectures, evaluate on live datasets, and publish results that advance both theory and practice.
• Contribute to the global research community shaping the future of structured AI and efficient learning.

What You’ll Bring

• PhD in Machine Learning, Statistics, Applied Mathematics, or a related field with specialization in structured, tabular, or relational data modeling .
• Research or applied work in areas such as representation learning, generalization theory, probabilistic modeling, or foundational models .
• Strong grounding in information theory, optimization, or statistical inference .
• Hands-on experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow, and proficiency in Python or Rust for large-scale experimentation.
• Demonstrated ability to translate theoretical ideas into performant, reliable systems.
• Curiosity about how structure and relational information can drive new forms of generalization and reasoning in AI.
• A pragmatic, impact-driven approach to research: you care about elegance, but you ship results that work at scale.

Bonus

• Research experience in structured representation learning, embeddings, or model architectures for tabular and multimodal data.
• Familiarity with distributed data systems, query engines, or large-scale learning infrastructure.
• Contributions to open-source projects or collaborative research bridging theory and production.

Why us

• Fundamental Research Meets Enterprise Impact. Work at the intersection of science and engineering, turning foundational research into deployed systems serving enterprise workloads at exabyte scale.
• AI by Design. Build the infrastructure that defines how efficiently the world can create and apply intelligence.
• Real Ownership. Design primitives that will underpin the next decade of AI infrastructure.
• High-Trust Environment. Deep technical work, minimal bureaucracy, shared mission.
• Enduring Horizon. Backed by NEA, Bain Capital, and various luminaries from tech and business. We are building a generational company for decades, not quarters or a product cycle.

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

Join us to build the foundational data systems that power the future of enterprise AI.

Here you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.