Agentic Coding Annotator Alwar

About Turing:

Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM, and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.

Role Overview:

We are looking for solid, detail-oriented software practitioners to help evaluate and improve datasets for agentic coding models.

This role involves working with realistic coding tasks in an agentic coding harness, reviewing model trajectories, verifying solutions, and producing high-quality annotations.

Depending on the assignment, the work may include:

Online evaluations: Manually interacting with blinded models on predefined tasks, then ranking and grading resulting trajectories

Offline evaluations: Designing realistic coding tasks, calibrating them through user simulation, writing task-specific rubrics, and grading generated trajectories

This is not a basic annotation role. Candidates are expected to read and debug code, validate behavior, follow detailed process rules, and make consistent judgment calls across model runs.

We are specifically looking for candidates with enough engineering maturity to independently work on realistic software tasks, not just toy problems or shallow code-review exercises.

What does day-to-day look like:

Execute realistic coding tasks within the assigned agentic coding harness while maintaining model blindness and session independence

Follow task instructions, milestones, planned interactions, and evaluation guardrails consistently across runs

Verify model outputs by reading code, running commands, checking logs, and inspecting generated artifacts

Perform targeted validation of outputs using tests, scripts, and manual checks

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