Member of Technical Staff: Research

AI has advanced by expanding what machines can represent.

Deep learning learned representations from raw data. Transformers gave machines access to the knowledge humanity compressed into language.

But descriptions are not experience.

Experts develop intuition by acting, observing consequences, and learning what matters. Models can consume descriptions of judgment, but they do not inherit the experience that produced it.

Observable Intuition is building the missing layer: infrastructure that makes human experience observable, learnable, and actionable by AI.

We deploy in the world’s largest enterprises, where judgment is exercised repeatedly and tested against reality. Its record already exists in fragments: decisions, revisions, exceptions, approvals, failures, and outcomes. We make that experience learnable, allowing our models to inherit the judgment organizations have developed through consequence.

Language gave machines access to what humanity has said about the world. Observable Intuition gives them access to what happened when people acted within it.

AI has advanced by expanding what machines can represent.

Deep learning learned representations from raw data. Transformers gave machines access to the knowledge humanity compressed into language.

But descriptions are not experience.

Experts develop intuition by acting, observing consequences, and learning what matters. Models can consume descriptions of judgment, but they do not inherit the experience that produced it.

Observable Intuition is building the missing layer: infrastructure that makes human experience observable, learnable, and actionable by AI.

We deploy in the world’s largest enterprises, where judgment is exercised repeatedly and tested against reality. Its record already exists in fragments: decisions, revisions, exceptions, approvals, failures, and outcomes. We make that experience learnable, allowing our models to inherit the judgment organizations have developed through consequence.

Language gave machines access to what humanity has said about the world. Observable Intuition gives them access to what happened when people acted within it.

As our Founding Research Scientist, you’ll define the research foundations behind this new layer of intelligence.

You’ll work alongside a founding team with deep experience building and deploying production foundation models at Fortune 500's from scratch. This is a hands‑on research role: you’ll develop new methods, build working systems, make foundational technical decisions, and help establish our research culture.

The central challenge is learning stable, useful, and calibrated representations of experience from incomplete, noisy, and longitudinal observations. State is distributed across systems, actions are interdependent, outcomes arrive late, and causal attribution is difficult.

You’ll define how behavior should be represented, how useful structure can be inferred from incomplete observations, and how uncertainty should be measured. You’ll then take those ideas from research into systems deployed within real enterprise environments.

• Build models that transform noisy observational data into structured representations.
• Develop methods for learning organizational behavior directly from real‑world activity.
• Model how organizational state and behavior evolve over time.
• Design uncertainty estimation and calibrated abstention systems.
• Create evaluation methodologies for problems where labels are sparse, ground truth is incomplete, and established benchmarks do not exist.
• Develop methods for evaluating the reliability under real‑world conditions of learned representations.
• Take research from papers and prototypes into deployed systems while maintaining scientific rigor under real‑world constraints.
• Work directly with the founders to shape our research direction, system architecture, and long‑term technical roadmap.

• A PhD or equivalent research depth in machine learning, statistics, computer science, or a related field.
• Research contributions in one or more of graph machine learning, knowledge representation, temporal or event modeling, process reconstruction, or…