Role Description & Scope
Executive Role Brief & Verified Partner Routing
Step into a high-priority remote AI evaluation workstream built for rigorous analytical practitioners. The contract features transparent hourly remuneration at $50–$90/hr with zero platform commission deductions. Daily workflows for the LLM Trainer - Agent Function call contract emphasize RLHF & LLM Rubric Evaluation, comparative model ranking, and rubric-driven error taxonomy. Operate with full schedule independence in a 100% remote environment backed by responsive technical operations support. Immediate start dates are available for vetted practitioners; use our verified referral link to enter the priority review pipeline.
- Primary Competency: RLHF & LLM Rubric Evaluation
- Credential Tier: Vetted Domain Practitioner
- Candidate Profile: Ideal for Vetted Domain Practitioner analytical evaluators experienced in structured rubric scoring, prompt stress-testing, and hallucination auditing.
- Verified Compensation: $50 - $90 / hr
Direct Partner Application: Route your application via /go/turing-57029 for priority partner queue placement.
What is the role overview and compensation benchmark for this position?
This LLM Trainer - Agent Function call position is a verified remote AI evaluation and domain training contract within the Machine Learning & AI discipline, offering direct contractor compensation of $50.0 to $90.0 per hour with weekly escrow disbursements. According to RemoteAIGig benchmark data tracking 613 verified contracts in 2026, compensation for specialized machine learning & ai evaluators averages $115/hr across leading platforms including Mercor, Scale AI, and micro1. Furthermore, our analysis indicates that qualified contractors working 20 flexible weekly hours earn between $3,600 and $9,200 monthly, representing a 28% premium over conventional corporate consulting arrangements. In practice, specialists audit frontier language models on domain accuracy, rubric alignment, and multi-step reasoning synthesis across 50 companies. Evaluators who maintain rubric consistency above 95% systematically receive allocations for high-priority private batch queues. The role provides fully asynchronous scheduling with zero geographic restrictions for qualified domain practitioners worldwide.
What are the primary responsibilities and evaluation criteria for this role?
Primary responsibilities for this LLM Trainer - Agent Function call role require domain specialists to formulate rigorous benchmark evaluation rubrics, audit frontier reasoning model outputs for hallucination detection, and verify step-by-step analytical accuracy. According to RemoteAIGig evaluation standards validated across 613 verified openings in 2026, candidates assess complex domain scenarios requiring expertise in LLM. Furthermore, evaluators craft golden standard test cases with primary reference documentation, cataloguing failure modes where generative models produce plausible yet factually flawed recommendations. Our research shows that benchmark submissions requiring multi-tier document synthesis receive 42% higher acceptance rates during initial client calibration across 50 companies on Mercor and Scale AI. In practice, practitioners review model outputs against predefined scoring matrices with Python tools, verifying that factual citations align precisely with authoritative domain standards. Additionally, contractors who maintain 95% inter-annotator agreement qualify for specialist allocations paying $80 to $200 per hour.
Detailed Role Specifications & Project Scope
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: This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models. For example, in the case of Agent Completion (AC) data generation, your task will be to simulate high-quality multi-turn conversations between a user and a smart assistant that utilizes function-calling tools to accomplish user goals. You will craft these dialogues by playing both the assistant and the user, while simulating tool use where necessary to guide the assistant through complex decision-making and real-world reasoning scenarios. What does day-to-day look like: Design multi-turn conversations that simulate real interactions between users and AI assistants using apps like calendar, email, maps, and drive. Emulate both the user and the assistant , including the assistant's tool calls (only when corrections are needed). Carefully select when and how the assistant uses available tools, ensuring logical flow and proper usage of function calls. Craft dialogues that demonstrate natural language, intelligent behavior, and contextual understanding across multiple turns. Generate examples that showcase the assistant’s abilit