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Turing Enterprises, Inc. is seeking a Software Engineering Evaluator to help build and refine datasets for training large language models.
The role centers on curating Python code, providing precise solutions, and correcting examples while collaborating with researchers to improve code quality and reliability across backend services and ML infrastructure. You will work mainly in Python, with JavaScript ReactJS, C/C++, Java, Rust, and Go, evaluating AI-generated code for efficiency and
Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in software engineering, logical reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.
This role is ideal for engineers who have built production systems at companies like Google, Microsoft, Apple, Amazon, Meta, or similar high-scale engineering organizations. We especially welcome graduates from top computer science programs such as Stanford, MIT, Carnegie Mellon, UC Berkeley, Georgia Tech, and comparable institutions — though exceptional experience and skill always take precedence over pedigree.
As a Software Engineering evaluator, you will create cutting-edge datasets for training, benchmarking, and advancing large language models, collaborating closely with researchers. This includes curating code examples, providing precise solutions, and making corrections — with a primary focus on Python across backend services, data pipelines, and ML infrastructure, alongside JavaScript (including ReactJS), C/C++, Java, Rust, and Go. You will evaluate and refine AI-generated code for efficiency, scalability, and reliability, and work with cross-functional teams to enhance enterprise-level AI-driven coding solutions.