Role Description & Scope
What is the role overview and compensation benchmark for this position?
This Member of Technical Staff, Model Training (post-training research) position is a verified remote AI evaluation and domain training contract within the Science & Research discipline, offering direct contractor compensation of $7 to $8 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 Member of Technical Staff, Model Training (post-training research) 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 Reinforcement Learning & Optimization, Post-Training Algorithms, Training Data Strategy & Curriculum Learning. 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
Job Title: Member of Technical Staff, Model Training (post-training research)
Job Type: Full-time
Location: Remote
The Role
In this role, you will investigate how learning algorithms, training data, and evaluation methodologies interact to produce stronger reasoning, generalization, and agent capabilities. You will explore new approaches to reinforcement learning, distillation, and data-efficient training, translating research insights into scalable methods that improve model intelligence.
This is a highly experimental role at the intersection of fundamental machine learning research and large-scale model development. You will have the opportunity to formulate research hypotheses, design ambitious experiments, and develop training methods that directly influence the capabilities of our models.
What You'll Do
- Research, implement, and scale advanced post-training methods, including supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), across large language and multimodal models.
- Investigate learning objectives, optimization dynamics, exploration strategies, and credit assignment mechanisms to improve training stability, sample efficiency, and capability acquisition.
- Develop and refine training recipes by jointly optimizing data mixtures, sampling strategies, curricula, and evaluation methodologies.
- Design methods for learning from expert demonstrations, human preferences, corrections, and model-generated trajectories, identifying where human feedback delivers the greatest improvements.
- Research the co-evolution of generators, reward models, and graders, developing approaches that improve both model performance and the quality of the training signals they consume.
- Design rigorous experiments, ablation studies, and evaluation frameworks to measure generalization, reasoning, and performance on realistic downstream tasks.
- Own research initiatives end-to-end, from identifying promising directions and implementing new algorithms to executing large-scale experiments and validating results.
What We're Looking For
- Strong foundations in machine learning, deep learning, optimization, and modern model training techniques.
- Hands-on experience training, fine-tuning, or post-training large-scale language or multimodal models.
- Deep technical expertise in one or more areas such as reinforcement learning, model distillation, reward modeling, data selection, or learning dynamics.
- Strong proficiency in Python and PyTorch, with the ability to implement, debug, and optimize complex training pipelines.
- Experience designing controlled experiments, interpreting model behavior, and translating empirical findings into actionable research directions.
- Ability to independently navigate ambiguous research problems, formulate hypotheses, and iterate quickly based on experimental evidence.
- Strong analytical thinking and a research-driven mindset, with an emphasis on reproducibility, scientific rigor, and measurable improvements.
Nice to Have
- Experience with RLHF, RLAIF, preference optimization, or reinforcement learning for reasoning and agentic tasks.
- Familiarity with distributed training, large-scale experimentation, and GPU-based model optimization.
- Experience developing reward models, verifiers, graders, or automated evaluation systems.
- Research contributions to post-training, reinforcement learning, or related areas through publications, open-source projects, or production systems.
- Experience investigating synthetic data generation, curriculum learning, or iterative self-improvement techniques.
Compensation & Benefits Notice
The national pay range for this full-time position is base salary of $200,000 –$320,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. our verified AI client provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.
our verified AI client is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@our verified AI client.ai.
Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.