Remote Ruby Tech Lead — AI Code Evaluation

turing; United States; Remote; USD 28,000 - 55,000; Full time

Benefits offered by this job

Fully remote work

Cutting-edge AI projects

Job summary

Turing is seeking an experienced software engineer at the tech-lead level to contribute to LLM evaluation datasets built from real-world public repositories. You will perform hands-on software engineering, automate environments, triage issues, and evaluate test coverage to ensure high-quality data.

Responsibilities include setting up repositories, Dockerization, running code locally, and collaborating with researchers to select challenging tasks for LLMs, with opportunities to lead a small team.

Qualifications

• 3+ years of overall software engineering experience.
• Strong Ruby experience or familiarity with at least one modern language.
• Proficiency with Git, Docker, and basic software pipeline setup.
• Ability to understand and navigate large, complex codebases.
• Comfortable running, modifying, and testing real-world projects locally.
• Experience contributing to or evaluating open-source projects is a plus.

Responsibilities

• Analyze and triage GitHub issues across trending open-source libraries.
• Set up and configure code repositories, including Dockerization and environment setup.
• Evaluate unit test coverage and quality.
• Modify and run codebases locally to assess LLM performance in bug-fixing scenarios.
• Collaborate with researchers to design and identify challenging repositories for LLMs.
• Lead a team of junior engineers on projects when needed.

Skills

Code navigation

Open-source experience

Team leadership potential

Bug fixing

Ruby

Tools

Git

Docker

CI/CD tooling

Job description

Turing is seeking an experienced software engineer at the tech-lead level to contribute to LLM evaluation datasets built from real-world public repositories. You will perform hands-on software engineering, automate environments, triage issues, and evaluate test coverage to ensure high-quality data.

Responsibilities include setting up repositories, Dockerization, running code locally, and collaborating with researchers to select challenging tasks for LLMs, with opportunities to lead a small team.