Distyl AI

AI Engineer

• Be the strategist in the room. Sit with CIOs, COOs, and their teams on where AI changes their business. Bring a point of view they can’t get from their own staff, and shape the technology and operating strategy — not a product pitch
• Understand the business cold. Learn how the customer makes money and where the friction is. Find the problems worth hundreds of millions to solve, and frame them in their language, not Distyl’s
• Turn strategy into outcomes. Translate executive conversations into real programs, mobilize Distyl’s forward-deployed engineers and researchers, and stay accountable to the metric that has to move
• Grow the relationship. Expand from a first program into a portfolio across business units and budgets. Done well, these become Distyl’s largest relationships — tens of millions a year and climbing
• Earn trust over the long run. Make the call that serves the customer even when it costs Distyl in the short term. That is what keeps a relationship compounding
• Be the connective tissue. Align Distyl’s delivery, product, and research teams behind the commitments you make, and give leadership a clear read — explanations and plans, not excuses

Lead AI Strategist

Research Engineer

• Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems
• Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases
• Investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints
• Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis
• Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems
• Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement
• Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability
• Communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs

Research Engineer, Post-Training

• 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

Research Engineer, Agent

Artificial Intelligence

• Enforce operational rigor
• Drive stakeholder alignment
• Operate at maximum velocity
• Operate as an AI-native
• Write the playbook

AI Operator – Research Programs

• 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

Lead AI Strategist

Research Engineer, Post-Training

Research Engineer, Agent

AI Operator – Research Programs

Research Engineer

Research Engineer, Data