Sr. Applied Scientist

Job Summary

Before an agent can reliably operate software on a users behalf, it needs to understand what should happen next and why: the underlying process, its steps, decision points and governing logic, branches, variations, dependencies, and long-tail edge cases. In practice, this knowledge is rarely represented explicitly. It is distributed across messy event logs, unstructured documents, screen recordings, UI interaction traces, historical executions, and the tacit knowledge of domain experts. As a Senior Applied Scientist on this team, you will provide technical leadership across teams and develop process-agnostic, scalable learning systems that recover the latent structure, decision logic, and dynamics underlying complex process execution trajectories from sparse, noisy, highly correlated observations spanning substantial behavioral heterogeneity and organizational scale. You will own significant research and technical areas, taking heterogeneous data such as action logs, noisy screen traces, semi-structured documents, and variant-heavy workflows and building systems that recover canonical process structure, meaningful variations, decision rules, dependencies, exceptions, and provenance.

You will shape the architecture of these systems and set evaluation standards for representations consumed by downstream agentic execution systems. Those representations must preserve enough detail for an agent to act correctly while remaining structured, compact, transparent, and auditable. The same observational data and learned representations also contribute directly to our work on computer-use models for last-mile interaction with enterprise software. The problems here rarely arrive with clean formulations. You may begin with a branch the system consistently fails to recover, two superficially different execution paths that should collapse into the same process, or an algorithm that works across thousands of traces but fails at millions. Your job is to turn those failures into tractable research programs: identify the right abstraction, formulate hypotheses, design evaluations that isolate the problem, and develop methods that solve it. You will lead ambiguous investigations, make consequential architecture and evaluation decisions, and mentor other scientists and engineers through the work. Depending on the problem, this may draw from process and decision mining, graph algorithms, structured prediction, representation learning, information retrieval, program synthesis, model post-training and distillation, or agentic systems.

We care less about allegiance to a particular technique than about finding the simplest method that reliably recovers the underlying structure, scales, and succeeds in production.

Responsibilities

Process discovery and representation

• Lead the design and development of pipelines that discover, extract, and formalize end-to-end business processes from event logs, UI interaction traces, screen recordings, and unstructured documents
• Develop and guide algorithms for process variant detection, noise removal, and trace consolidation, turning noisy, high-cardinality action sequences into clean canonical representations
• Own core representation and architecture decisions so downstream agents receive the information they need while irrelevant execution-level variation is collapsed
• Study and establish principles for the tradeoff between abstraction and fidelity: determine when superficially different trajectories represent the same underlying behavior and when small differences encode materially different decisions
• Build retrieval and grounding systems that incorporate SOPs, documents, and domain knowledge when process understanding requires evidence beyond execution traces
• Set technical direction for major research areas, review experimental evidence, and mentor scientists and engineers working on related methods

Evaluation and reliability

• Own rigorous evaluation frameworks for process discovery and extraction, including LLM-as-Judge pipelines, conformance checking against ground-truth processes, and regression suites that catch subtle degradation
• Develop evaluation methods for settings where there may not be a single canonical ground truth, including behaviorally equivalent process representations
• Define quality bars and systematically test exception paths, conditional branches, loops, rare variants, and the failure modes that naive extraction approaches miss
• Make and communicate deliberate algorithmic and architectural tradeoffs so systems remain tractable across millions of traces and thousands of variants, including choices around data structures, approximation, indexing, and level of abstraction
• Diagnose failures in production, turn recurring failure patterns into new algorithmic or research directions, and drive improvements through validation and deployment

Collaboration

• Work closely with the computer-use agents team so the representations you produce are expressed at the right level of abstraction, contain the necessary execution context, and are robust enough for downstream autonomous execution
• Lead technical collaboration across research, engineering, and product to move promising methods from prototype to production and measure their effect on downstream agent behavior
• Mentor other scientists and engineers through problem formulation, experimental design, technical reviews, and productionization without serving as their people manager
• Represent the teams work through high-quality technical documents, external publications where appropriate, and clear communication with technical and non-technical stakeholders

Requirements

• Ambitious and Hungry. Far and away the best prize that life offers is the chance to work hard at work worth doing. - Theodore Roosevelt
• We screen for comparable experience and strong fundamentals, not exact tool matches. Equivalent technologies are welcome.
• Problem Decomposition and Algorithmic Thinking. You can take a loosely defined problem, such as recovering a reliable process from messy action logs, decompose it into tractable sub-problems, reason carefully about edge cases, and arrive at an approach. You are comfortable making and defending architecture decisions grounded in first principles about data structures, graph algorithms, approximation, indexing, and time and space complexity when algorithms must operate over millions of executions.
• ML / DL Breadth. Deep working knowledge across representation learning, sequence modeling, clustering, structured prediction, information retrieval, generative modeling, and modern foundation-model adaptation. You have the judgment to recognize when a problem calls for a learned model, an algorithmic solution, an LLM-based system, or some combination of the three, and can guide others through those choices.

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