JavaScript Developer

Turing•Austin, TX

4h•Remote

About The Position

Turing is seeking a Software Engineering evaluator to create cutting-edge datasets for training, benchmarking, and advancing large language models. This role involves curating code examples, providing precise solutions, and making corrections in various programming languages, with a particular emphasis on systems-level code, performance-critical applications, and infrastructure. The evaluator will also assess and refine AI-generated code for efficiency, scalability, and reliability, and collaborate with cross-functional teams to enhance enterprise-level AI-driven coding solutions.

Requirements

Several years of software engineering experience (3 years or more).
Strong expertise in systems programming, infrastructure, or backend development using languages like JS, Python, C/C++, Rust, and Go.
Experience building and 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.
Candidates must be based in the United States.

Nice To Haves

Experience at companies like OpenAI, NVIDIA, Databricks, Palantir, Snowflake, or similar organizations pushing the boundaries of intelligent systems.
Graduates from leading programs such as Harvard, Columbia, Princeton, Yale, University of Pennsylvania, and comparable institutions.

Responsibilities

Work on AI model training initiatives by curating code examples, building solutions, and correcting code in Python, C/C++, Rust, Go, Java, and JavaScript (including ReactJS).
Evaluate and refine AI-generated code with an emphasis on systems-level correctness, performance, and reliability.
Collaborate with cross-functional teams to enhance AI-driven coding solutions against industry performance benchmarks.
Build agents that can verify the quality of systems-level and infrastructure code and identify error patterns.
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.