Python/Typescript Engineer

Turing•Austin, TX

2d•Remote

About The Position

Turing is an AGI infrastructure company that works with frontier AI labs to accelerate model development through training data, evaluations, and engineering talent. The role involves designing and refining rubrics and evaluation criteria for preference data, reviewing and labeling data to a high quality standard, and building and maintaining infrastructure and pipelines for data workflows. The engineer will synthesize findings into clear updates or recommendations and collaborate with researchers and engineers to turn qualitative judgment into scalable processes. The role is fully remote and is offered as an independent contractor engagement for three months, with an expected start as soon as possible. The preferred availability is 40 hours per week, with at least six hours of overlap with Pacific Time; applicants who cannot commit to 40 hours should provide their available hours and overlap window. Compensation is market rate, and applicants are asked to specify a single rate expectation. The evaluation process is approximately 30 minutes and consists of one technical and cultural discussion.

Requirements

Proficiency in Python, especially building and maintaining pipelines and infrastructure, or Typescript.
Hands-on experience with preference data collection and/or rubric creation.
Strong written and verbal communication skills, including the ability to distill technical work into clear, actionable summaries.
High-taste judgment and the ability to articulate why something is good, not just that it is.
Availability is preferred at 40 hours per week, with a minimum six-hour overlap with Pacific Time; applicants unable to commit to 40 hours should specify their availability and overlap window.
Provide a specific rate expectation rather than a range.

Nice To Haves

Experience contributing to or navigating large codebases such as VSCode.

Responsibilities

Design, refine, and iterate on rubrics and evaluation criteria for preference data.
Review and label data while identifying subtle quality issues.
Build and maintain pipelines and infrastructure supporting data generation, collection, and evaluation workflows.
Synthesize findings from data work into clear write-ups, updates, or recommendations.
Collaborate with researchers and engineers to translate qualitative judgment into scalable processes.