Rust Engineer - AI Data Training

Role : Rust Engineer - AI Data Training

Location : Remote

Required Skillsets:

• 1–2+ years of professional Rust development experience in backend, CLI, or systems-focused projects.
• Strong understanding of Rust’s ownership, borrowing, and lifetime model, with the ability to reason clearly about aliasing and data races.
• Solid software engineering experience in at least one of backend services, command-line tools, or systems programming using Rust.
• Ability to evaluate safe, idiomatic Rust code, including appropriate use of traits, generics, pattern matching, and error handling.
• Significant experience using LLMs or AI coding assistants while programming, combined with a disciplined approach to validating their output.
• Excellent English writing skills, capable of producing precise, structured, and pedagogical technical explanations.

Role Description: The candidate will review AI-generated Rust code and explanations or generate your own, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained.

You will assess solutions for correctness, safety, and adherence to the prompt; identify errors in ownership, borrowing, lifetimes, or algorithmic reasoning; fact-check information; write high-quality explanations and model solutions that demonstrate idiomatic Rust patterns; and rate and compare multiple AI responses based on correctness and reasoning quality.

This role is with, a fast-growing AI Data Services company and subsidiary of that provides AI training data for many of the world’s largest AI companies and foundation model labs. Your work will directly help improve the world’s premier AI models while giving you the flexibility of impactful, detail-oriented remote contract work.

Key Responsibilities:

• Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of

diverse subjects.

• Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
• Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.