Research Engineer, QC Automation — AI Training Data Quality

Dreams 2 Reality Recruitment; San Francisco (CA); On-site; USD 100,000 - 200,000; Full time

Job summary

Dreams 2 Reality Recruitment is seeking a Research Engineer, QC Automation in the San Francisco Bay Area to build robust QC infrastructure for AI training data. You will design automated QA/QC pipelines, define quality standards, and collaborate with data vendors to identify and fix agent failure modes.

The role emphasizes autonomy, rapid learning, and turning ambiguous quality requirements into scalable, measurable systems within an early-stage startup environment.

Qualifications

• Strong proficiency in Python, Docker, and Linux.
• Experience building scalable data-validation pipelines and automated QA/QC systems.
• Ability to design benchmarks and evaluations for AI data quality.
• Comfort working in an early-stage startup environment and taking ownership.
• Strong written and verbal communication; curious and fast learner.

Responsibilities

• Build quality-control systems grounded in human judgment and data quality.
• Define, formalize, and enforce quality standards for training data.
• Design experiments, benchmarks, and metrics to evaluate agent performance.
• Partner with data vendors to identify failure modes and improve data-generation processes.
• Build systems for auditing supplier datasets, including sampling and validation approaches.
• Integrate QC insights into HUD infrastructure and vendor portal to reduce anomalies.

Skills

Python

Data quality

QA/QC

Experiment design

Statistics

Learning agility

Startup experience

Communication

Tools

Python

Docker

Linux

Job description

Dreams 2 Reality Recruitment is seeking a Research Engineer, QC Automation in the San Francisco Bay Area to build robust QC infrastructure for AI training data. You will design automated QA/QC pipelines, define quality standards, and collaborate with data vendors to identify and fix agent failure modes.

The role emphasizes autonomy, rapid learning, and turning ambiguous quality requirements into scalable, measurable systems within an early-stage startup environment.