Role Description & Scope
What is the role overview and compensation benchmark for this position?
This Member of Technical Staff, Model Training (applied research) position is a verified remote AI evaluation and domain training contract within the Robotics 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 robotics evaluators averages $91/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 (applied 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 Model Training & Adaptation, Multimodal AI, Robotics & Agent 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 (applied research)
Job Type: Full-time
Location: Remote
The Role
In this role, you will work across language models, multimodal systems, robotics, and autonomous agents, developing models optimized for specific applications and environments. You will own the full model development lifecycle, from understanding application requirements and designing training datasets to model adaptation, evaluation, and production deployment.
Our objective is to build specialized models that match or exceed the quality of frontier models on targeted workloads while significantly improving inference efficiency, latency, and cost.
You will operate at the intersection of applied machine learning research and production engineering, turning advances in model training into reliable, high-performance systems with measurable real-world impact.
What You'll Do
- Train, fine-tune, and adapt language, vision-language, and robotics models using supervised fine-tuning (SFT), reinforcement learning (RL), imitation learning, and distillation.
- Develop specialized models for complex applications, including multimodal understanding, robotic perception and control, and autonomous agents capable of reasoning, planning, and tool use.
- Build simulation-based training and evaluation environments to study policy learning, task generalization, and the transfer of capabilities from simulated to real-world settings.
- Combine expert demonstrations, human feedback, synthetic datasets, and environment-generated trajectories into effective training datasets, reward signals, and learning curricula.
- Develop specialized models that power our verified AI client's flow ecosystem, improving data generation, annotation, quality assurance, and automated grading workflows.
- Incorporate expert corrections and model performance feedback into iterative training cycles to continuously improve accuracy, robustness, and task success.
- Optimize models for production inference, balancing quality, latency, throughput, and computational efficiency against frontier-model baselines.
- Collaborate with research and platform engineering teams to transition promising prototypes into scalable, production-ready model deployments.
What We're Looking For
- Strong foundations in machine learning and deep learning, with hands-on experience training, adapting, and evaluating modern AI models.
- Practical experience in one or more areas, including large language models, vision-language models, robotics, reinforcement learning, or autonomous agents.
- Strong proficiency in Python and PyTorch, with the ability to develop reliable training, evaluation, and inference pipelines.
- Experience translating application requirements into measurable model objectives, benchmarks, and performance improvements.
- Solid software engineering fundamentals, including the ability to build maintainable systems and move research prototypes toward production.
- Familiarity with model adaptation techniques such as fine-tuning, distillation, imitation learning, or reinforcement learning.
- Strong experimental judgment and the ability to independently identify performance bottlenecks, evaluate trade-offs, and iterate toward effective solutions.
Nice to Have
- Experience with robotics, embodied AI, vision-language-action models, or simulation-based policy learning.
- Familiarity with agent frameworks, tool-using models, and environment-driven training.
- Experience with synthetic data generation, human-in-the-loop learning, or automated evaluation systems.
- Knowledge of inference optimization techniques, including quantization, batching, model compression, and efficient serving.
- Experience deploying specialized models in production environments with demanding latency, reliability, or cost requirements.
- Research contributions or open-source work in applied machine learning, multimodal AI, robotics, or agent systems.
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.