Remote Python & TypeScript Engineer—AI Data Pipelines

Turing; San Francisco (CA); Remote; USD 83,000 - 138,000; Part time

Benefits offered by this job

Fully remote

Job summary

Turing is seeking a high-taste engineer focused on preference data and rubric design to help scale evaluation pipelines for AI systems. You will craft rubrics, label data with a meticulous standard, and build robust data pipelines aligned with research goals.

You will collaborate with researchers and engineers, translating qualitative judgments into repeatable processes, while thriving in a fast-moving, ambiguous environment and delivering actionable insights.

Qualifications

• Hands-on experience with preference data collection and rubric creation
• Proficiency in Python (pipelines & infra) or TypeScript (large codebases)
• Strong written and verbal communication—able to distill technical work into clear, actionable summaries
• Demonstrated high-taste judgment—able to articulate why something is good, not just that it is.

Responsibilities

• Design, refine, and iterate on rubrics and evaluation criteria for preference data
• Review and label data with a high bar for quality, catching subtle issues others might miss
• Build and maintain pipelines/infra that support data generation, collection, and evaluation workflows
• Synthesize findings from data work into clear write-ups, updates, or recommendations for the team
• Collaborate closely with researchers/engineers to translate qualitative judgment into scalable processes

Skills

Python pipelines/infra

TypeScript codebases

Preference data collection

Rubric creation

Strong communication

High-taste judgment

Tools

VSCode

Job description

Turing is seeking a high-taste engineer focused on preference data and rubric design to help scale evaluation pipelines for AI systems. You will craft rubrics, label data with a meticulous standard, and build robust data pipelines aligned with research goals.

You will collaborate with researchers and engineers, translating qualitative judgments into repeatable processes, while thriving in a fast-moving, ambiguous environment and delivering actionable insights.