Role Description & Scope
What is the role overview and compensation benchmark for this position?
This Member of Technical Staff, Model Training (infra & platform) position is a verified remote AI evaluation and domain training contract within the Cloud & DevOps 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 cloud & devops evaluators averages $95/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 (infra & platform) 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 Distributed ML Training Infrastructure, GPU Performance & Inference Optimization, Distributed Systems & Platform Architecture. 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 (infra & platform)
Job Type: Full-time
Location: Remote
The Role
In this role, you will take technical ownership of the distributed systems and platforms that enable researchers to train increasingly capable models, execute large-scale experiments, and deploy specialized AI systems efficiently.
You will work across distributed training, GPU infrastructure, inference optimization, simulation environments, and agent execution frameworks, making critical architectural decisions that directly influence research velocity and model performance.
What You'll Do
- Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), supporting language, multimodal, and robotics workloads.
- Design distributed training and execution pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
- Develop high-performance infrastructure for model training and inference, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
- Build and evolve the core architecture of our verified AI client's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
- Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve research reproducibility and operational reliability.
- Identify and eliminate infrastructure bottlenecks, reducing experiment turnaround time and enabling researchers to run increasingly complex training workloads.
- Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
- Lead critical engineering initiatives from system design through implementation and production operations, mentoring engineers while maintaining direct ownership of technically challenging components.
What We're Looking For
- Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
- Extensive experience designing, building, and operating scalable backend systems or distributed computing platforms.
- Proficiency in Python and at least one systems-oriented programming language, such as C++, Rust, or Go.
- Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
- Strong understanding of system performance, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
- Experience designing reliable APIs, services, and infrastructure components that support complex engineering or research workflows.
- Demonstrated technical ownership, including making architectural decisions, leading cross-functional initiatives, and delivering systems that other engineers depend on.
- Ability to operate effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.
Nice to Have
- Experience with distributed ML frameworks and technologies such as PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar systems.
- Familiarity with GPU inference engines and optimization frameworks such as vLLM, TensorRT-LLM, or equivalent technologies.
- Experience building reinforcement learning infrastructure, rollout orchestration systems, or distributed agent execution environments.
- Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
- Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.
- Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation systems.
- Contributions to open-source infrastructure, distributed computing frameworks, or high-performance machine learning 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.