Applied AI Data Systems Engineer

Distyl; New York (NY); Hybrid; USD 150,000 - 250,000; Full time

Benefits offered by this job

Equity

Medical, dental, vision insurance

Flexible time off

401(k) and financial planning

Job summary

Distyl is seeking Research Engineers to bridge frontier AI research and production systems. You will design data systems and pipelines, collaborate with AI Researchers, and implement robust data quality and labeling workflows for enterprise AI deployments.

You will work at the intersection of applied research and customer deployment, turning new techniques into reliable systems. The role emphasizes measurable data quality, production readiness, and cross-functional collaboration in a hybrid

Qualifications

• Built data pipelines, evaluation datasets, labeling workflows, or retrieval corpora to improve model or agent behavior.
• Strong Python and SQL coding skills for production data systems.
• Experience building data quality frameworks and data-driven evaluation processes.

Responsibilities

• 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 data quality impacts on system behavior and outcomes.
• Develop synthetic data, annotation, and feedback-loop strategies to improve performance where real data is sparse or noisy.
• Analyze customer workflows and datasets to determine information needs and representations.

Skills

Data pipelines

Python

SQL

Data quality

Experimentation

Job description

Distyl is seeking Research Engineers to bridge frontier AI research and production systems. You will design data systems and pipelines, collaborate with AI Researchers, and implement robust data quality and labeling workflows for enterprise AI deployments.

You will work at the intersection of applied research and customer deployment, turning new techniques into reliable systems. The role emphasizes measurable data quality, production readiness, and cross-functional collaboration in a hybrid