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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Engineers, Data - **Company:** Distyl AI - **Location:** San Francisco, CA, United States - **Salary:** $150,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Customer Data Management, Information Engineering, Data Governance, Data Systems, Software Debugging, Python (Programming Language), SQL Databases, Data Layers, Build Management, Build Tools, Data Pipelines, Automation Anywhere - **Published:** June 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=d3ee798341e10fcc ## About the Role Do you have experience in Customer communication?, * Experience Building Data Systems for AI: You have built data pipelines, evaluation datasets, labeling workflows, retrieval corpora, or similar systems that improve model or agent behavior * Strong Data Engineering Fundamentals: You write clean Python and SQL, understand data modeling and pipeline reliability, and can build systems that are maintainable under production constraints * Research-Oriented Builder: You are comfortable investigating how data quality, structure, and representation affect AI system performance * AI-Native Working Style: You use AI tools daily to accelerate coding, analysis, debugging, exploration, and workflow automation * Comfort with Ambiguous Data: You can reason through messy enterprise datasets, incomplete documentation, conflicting business definitions, and changing requirements * Bias Towards Measurement: You prefer to make data quality and system behavior observable through concrete metrics, evaluations, and experiments * Customer Environment Readiness: You can work directly with customer teams to understand their data, ask precise questions, and explain tradeoffs clearly * Ownership Mentality: You take responsibility for whether the data layer enables the AI system to deliver reliable value in production ## Description Research Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production., * 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 ## Related Videos - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data: The Deciding Factor in AI Success](https://www.wearedevelopers.com/videos/100310-data-the-deciding-factor-in-ai-success) - [How to govern Vibe Coding for the Enterprise](https://www.wearedevelopers.com/videos/100290-how-to-govern-vibe-coding-for-the-enterprise) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Quality Strategy with a side of Swiss Cheese](https://www.wearedevelopers.com/videos/467-quality-strategy-with-a-side-of-swiss-cheese) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)