Job Title: Fullstack Engineer – AI/LLM Code Evaluation & Development (Remote – US, Canada)
Domain: Information Technology (IT)
Job Type: Contract (1 Month, Possible Extensions)
Location: Remote – Must reside in the US, Canada,
Experience Required: 5–10 years

MUST-HAVE QUALIFICATIONS

• Experience:
• 5+ years of software engineering experience.
• Minimum 2 continuous years as a full-time employee at a top-tier product/tech company (e.g., Google, Meta, Amazon, Microsoft, etc.) – Refer list
• Technical Expertise:
• Strong in full-stack application development.
• Deep understanding of software architecture, design patterns, debugging, and scalable systems.
• Proven ability to assess code diffs for correctness, maintainability, and performance.
• Communication:
• Excellent written and verbal skills for clear and structured feedback.
• Location & Work Eligibility:
• Must be a citizen/permanent resident/work permit holder in the US, Canada,
• H-1B visa holders are not eligible.
• LinkedIn Requirement:
• Verified LinkedIn profile with visible activity, creation date, and connections.
• LinkedIn URL must be included in the resume.

ABOUT THE ROLE

Turing, one of the fastest-growing AI companies globally, is looking for experienced software engineers to work on cutting-edge AI-assisted software development projects. You will contribute to building high-quality datasets that enhance the performance of Large Language Models (LLMs) in software engineering tasks. This includes reviewing AI-generated code, building intelligent agents, and helping AI reason through real-world software.

KEY RESPONSIBILITIES

• Collaborate on diverse AI/LLM projects to improve model performance in code understanding and generation.
• Lead development of agent-based use cases (e.g., coding copilots, automation bots).
• Analyze and evaluate AI-generated code for correctness, quality, and performance.
• Rank multiple model outputs using a structured evaluation system.
• Provide detailed, well-reasoned feedback and rationale for code evaluations.
• Identify edge cases, ambiguities, and improvement areas in model behavior.