Applied Scientist

About the Role

Before an agent can reliably operate software on a user's 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. 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.

You will shape the architecture of these systems and set evaluation standards for representations consumed by downstream agentic execution systems. The same observational data and learned representations also contribute directly to our work on computer-use models for last-mile interaction with enterprise software.

Responsibilities

• 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
• Own core representation and architecture decisions for downstream agents
• Study the tradeoff between abstraction and fidelity in process representations
• Build retrieval and grounding systems that incorporate SOPs, documents, and domain knowledge
• Set technical direction for major research areas and mentor scientists and engineers
• Own rigorous evaluation frameworks for process discovery and extraction, including LLM-as-Judge pipelines, conformance checking, and regression suites
• Define quality bars and systematically test exception paths, conditional branches, loops, and rare variants
• Work closely with the computer-use agents team on representation design
• Communicate technical tradeoffs and represent the team's work through technical documents and publications

Requirements

• Comparable experience and strong fundamentals in problem decomposition and algorithmic thinking, including data structures, graph algorithms, approximation, and indexing
• Deep working knowledge across representation learning, sequence modeling, clustering, structured prediction, information retrieval, generative modeling, and foundation-model adaptation
• Ability to read and adapt research from frontier AI, process mining, program synthesis, information retrieval, and graph learning
• Research judgment and leadership, with the ability to define research directions and design isolating experiments
• Extensive hands-on experience building reliable systems with modern language or multimodal models, including retrieval, structured generation, evaluation, and grounding
• Excellent Python software engineering skills and experience with ML frameworks such as PyTorch
• Strong communication skills for both researchers and product managers

Preferred Experience

• Sustained research track record in top-tier machine learning, AI, NLP, computer vision, or data mining venues
• Experience taking research from published work through production deployment
• Process mining, information extraction from documents, and multimodal data processing
• Foundation-model adaptation, including supervised fine-tuning, LoRA, distillation, and reinforcement-learning-based post-training