Backend Engineer (Python/FastAPI)

About Us:

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.

Ideal Background:

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 leading programs such as Harvard, Columbia, Princeton, Yale, University of Pennsylvania, and comparable institutions — though exceptional experience and skill always take precedence over pedigree.

Project Overview:

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 across the full stack — in Python for backend and ML workflows, and JavaScript (React, Node.js) for frontend and API layers, alongside 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.

What Does a Typical Day Look Like?

• Work on AI model training initiatives by curating code examples, building solutions, and correcting code across both Python and JavaScript (React, Node.js), with additional work in C/C++, Java, Rust, and Go.
• Evaluate and refine AI-generated code across backend and frontend contexts to ensure that it is efficient, scalable, and reliable.
• Collaborate with cross-functional teams to enhance AI-driven coding solutions against industry performance benchmarks.
• Build agents that can verify the quality of the code and identify error patterns across full-stack applications.
• Hypothesize on steps in the software engineering cycle (prototyping, architecture design, API design, production implementation, launch, experiments, monitoring, operational maintenance) and evaluate model capabilities on them.
• Design verification mechanisms that can automatically verify a solution to a software engineering task.

Required Skills:

• Several years of software engineering experience (3 years or more).
• Strong expertise in building full-stack applications using Python and JavaScript (React, Node.js), with the ability to work across backend and frontend codebases.
• Experience deploying scalable, production-grade software using modern languages and tools.
• Deep understanding of software architecture, design, development, debugging, and code quality/review assessment.
• Excellent oral and written communication skills for clear, structured evaluation rationales.

Engagement Details:

• Commitment: flexible engagement, minimum 10 hrs/week, up to 40 hrs/week.
• Type: Contractor (no medical/paid leave).
• Duration: 1 month (potential extensions based on performance and fit).
• Location: Candidates must be based in the United States.

Evaluation Process:

• The application process takes 15–30 minutes.
• Completion of an AI video interview is required.

About Turing:

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 coding, 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.