Role Description & Scope
Executive Role Brief & Verified Partner Routing
This verified remote AI evaluation contract offers an immediate opportunity for specialized domain experts. Verified remuneration is structured at $50–$95/hr, rewarding high-precision rubric adherence and analytical depth. Contributors in the Computer Science (STEM) role leverage Python & Backend Engineering to benchmark model performance across high-difficulty domain scenarios. Contributors enjoy complete geographic freedom and asynchronous task queues that fit seamlessly around existing commitments. Immediate start dates are available for vetted practitioners; use our verified referral link to enter the priority review pipeline.
- Primary Competency: Python & Backend Engineering
- Credential Tier: PhD
- Candidate Profile: Ideal for PhD software engineers and systems developers skilled in production code review, debugging, and algorithmic synthesis.
- Verified Compensation: $50 - $95 / hr
Direct Partner Application: Route your application via /go/turing-117402 for priority partner queue placement.
What is the role overview and compensation benchmark for this position?
This Computer Science (STEM) position is a verified remote AI evaluation and domain training contract within the Science & Research discipline, offering direct contractor compensation of $50.0 to $95.0 per hour with weekly escrow disbursements. According to RemoteAIGig benchmark data tracking 613 verified contracts in 2026, compensation for specialized science & research evaluators averages $82/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 Computer Science (STEM) 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 Python. 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
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent. Engagement Details Commitments Required: At least 6 hours per day and a minimum of 40 hours per week, with a 4-hour overlap with PST. Employment Type: Contractor assignment; no medical or paid leave. Duration of Contract: 5 weeks; expected start date is next week. Payment Type: $280 per approved task. About the RoleWe are seeking Computational Life Sciences experts with strong scientific programming skills to develop complex, realistic tasks for Agentic Life Sciences. You will design tasks where AI agents must independently navigate scientific data, write and execute code, use computational tools, troubleshoot intermediate results, and produce scientifically valid final outputs. The goal is to evaluate whether AI systems can perform authentic, multi-step scientific work—not simply answer scientific questions. Key Responsibilities Design challenging, realistic agentic scientific workflows across the life sciences. Create tasks requiring multiple computational steps rather than single-script or simple question-and-answer solutions. Develop realistic input files, scientific datasets, instructions, constraints, and expected deliverables. Build tasks requiring agents to inspect data, select appropriate methods, execute analyses, troubleshoot problems, and synthesize results. Create reproducible expert solutions and objectively verifiable ground truths that run in the provided sandbox environment. Design robust automated or semi-automated grading criteria. Ensure tasks measure scientific reasoning and execution rather than memorization. Validate scientific assumptions, calculations, code, intermediate outputs, and final answers. Ensure tasks are self-contained and executable in controlled, network-isolated computational environments. Maintain high quality and throughput while responding effectively to reviewer feedback. Qualifications Required Ph.D., postdoctoral, or equivalent research experience in the life sciences, with strong demonstrated computational and scientific programming experience. Strong scientific programming experience, particularly in Python. Experience performing multi-step computational scientific analyses. Ability to independently validate both scientific reasoning and computational outputs. Preferred Qualifications Expertise in one or more areas, including bioinformatics, computational genomics, systems biology, computational neuroscience, biostatistics, computational drug discovery, computational biochemistry, structural biology, protein engineering, or computational microbiology. Experience with scientific libraries and command-line tools. Experience creating reproducible research pipelines. Ability to translate authentic research workflows into bounded, objectively gradable tasks.