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Remote role, generalist software title, mid-level experience requirement, and known employer increase candidate competition.
Ruby backend and open-source evaluation experience moderately transferable but favors engineering backgrounds with repo and testing expertise.
Explicit minimum experience plus required Ruby, Git, and Docker skills enforce moderate shortlisting filters.
Lead and perform hands-on software engineering to build and evaluate LLM training datasets based on real-world public GitHub repositories.
Automate development environments including Dockerization, triage issues, and assess unit test coverage and quality in complex codebases.
Collaborate with researchers and potentially lead junior engineers to identify and work on challenging software engineering tasks for LLM evaluation.
Minimum 3+ years of software engineering experience.
Strong proficiency in Ruby programming language.
Experience with Git, Docker, and software pipeline setup.
Must be able to work minimum 20 hours per week with at least 4 overlapping hours with PST (Contractor assignment).
Experienced with high-quality public open-source repositories and comfortable navigating and modifying real-world complex codebases locally.
Demonstrated ability to analyze and triage issues in trending open-source libraries and evaluate test coverage.
Capable of integrating software engineering practices with AI research initiatives, supporting LLM evaluation and training datasets.