Distyl AI

Research Engineer

• Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows
• Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution
• Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints
• Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes
• Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve
• Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use
• Integrate agents into customer APIs, applications, data platforms, and operational workflows
• Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks

• Design and build data systems that power reliable AI workflows across enterprise environments
• Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems
• Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes
• Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution
• Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes
• Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy
• Analyze customer workflows and datasets to determine what information AI systems need, where that information should come from, and how it should be represented
• Communicate clearly with internal teams and customer stakeholders about data assumptions, limitations, risks, and tradeoffs

Research Engineer, Data

AI Research Scientist

• The Multi-Agent Systems team focuses on designing architectures in which multiple agents coordinate to solve problems that require structured interaction across multiple reasoning processes. Researchers build systems that structure communication, route information, and coordinate decision-making across agents operating with different views of the problem
• Researchers in Multi-Agent Systems investigate the interaction patterns that govern how agents collaborate. They study how agents exchange information, critique and refine each other’s reasoning, and coordinate execution across complex workflows. Their work identifies the mechanics behind effective communication, delegation, and coordination, in effect establishing the design language for how systems of agents can operate as cohesive, high-performing teams, with capabilities that arise from interaction rather than individual performance.

Applied AI Researcher – Multi-Agent Systems

Artificial Intelligence

• Own the operating rhythm and enforce accountability across various business functions
• Drive operational execution for revenue, platform delivery, and research programs
• Collaborate with sales and business development leadership
• Track goals, remove blockers, and drive clarity in operations
• Create visibility and reporting systems for program health and risk

AI Operator

• Lead complex, large-scale AI implementations at Fortune 500 clients
• Ensure goals are clear and commitments are kept
• Establish and sustain trust with enterprise clients
• Navigate client executives, technical teams, and Distyl leadership with confidence
• Keep programs moving in fast-changing environments
• Onboard and organize forward-deployed engineers and AI strategists
• Author best practices for delivering AI programs at scale

AI Operator – Implementation Delivery

AI Engineer

• Own the behavior and performance of AI systems deployed for customers
• Design and implement compound AI workflows that combine models, agents, retrieval, evaluation, and execution into coherent, production-ready systems aligned with real SME needs
• Operate on live systems, measuring behavior, identifying failure modes, and iterating rapidly to improve quality, reliability, and usefulness.
• Integrate AI systems into customer data platforms, APIs, and existing applications.
• Work directly with customer stakeholders—often in high-visibility settings—and are expected to reason clearly about system behavior, tradeoffs, and limitations.
• Take accountability for outcomes in production and adapt systems as requirements evolve

Frontend Engineer

• Design and ship interfaces where operators interact with AI systems to perform real operational work. These applications may include: knowledge capture tools, evaluation and feedback workflows, AI configuration and control interfaces, workflow orchestration tools, and custom applications for enterprise use cases
• Collaborate closely with AI Strategists and customer domain experts to understand real workflows
• Translate those workflows into working product experiences that allow operators to interact with and guide AI systems
• Move quickly from idea → prototype → production
• Use real user interactions and system behavior to refine both the product experience and the AI systems it supports
• Integrate applications with LLMs, evaluation pipelines, and internal AI infrastructure
• Understand how interface design affects: system behavior, feedback quality, user trust, system improvement loops

Frontend Engineer – Product

Backend Engineer

• Help design, build, and optimize Distillery—our AI-native platform
• Develop scalable AI infrastructure, ensuring system reliability
• Collaborate with engineers and business leaders to tackle complex AI deployment challenges
• Build & Scale AI-Native Infrastructure
• Develop Cloud-Native Microservices & Scalable AI Systems
• Optimize System Performance & Reliability
• Collaborate with Cross-Functional Teams