Python/Typescript Engineer

Turing•Chicago, IL

2d•Remote

About The Position

Turing is an AGI infrastructure company working with frontier AI labs to accelerate model development through training data, evaluations, and engineering talent. The role is for an engineer with strong quality judgment and hands-on experience with preference data and rubric creation. The engineer should communicate complex technical work clearly and concisely, work effectively in ambiguous and fast-moving environments, and prioritize shipping useful work. The position involves creating and refining rubrics and evaluation criteria, reviewing and labeling data, and building and maintaining infrastructure and pipelines for data generation, collection, and evaluation. The engineer will synthesize findings into clear updates and recommendations and collaborate with researchers and engineers to turn qualitative judgment into scalable processes. This is a fully remote, three-month independent contractor engagement, with an expected start as soon as possible. Compensation is market rate; applicants are asked to provide a specific rate expectation. A 40-hour workweek is preferred, with at least six hours of overlap with Pacific Time. The evaluation process is approximately 30 minutes and consists of one technical and cultural discussion. The engagement does not include medical or paid leave benefits.

Requirements

Proficiency in Python, especially for building and maintaining pipelines and infrastructure, or proficiency in 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.

Nice To Haves

Experience contributing to or navigating large codebases such as VSCode.
Availability for 40 hours per week is preferred.
At least six hours of overlap with Pacific Time is preferred.

Responsibilities

Design, refine, and iterate on rubrics and evaluation criteria for preference data.
Review and label data to a high quality standard, catching subtle 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.