Member of Technical Staff, Model Training (Infra & Platform) – $7–$8 per hour Remote Openings 01

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Member of Technical Staff, Model Training (Infra & Platform) – Remote Job at micro1

micro1 is listing a full-time remote opportunity for a Member of Technical Staff, Model Training (Infrastructure & Platform). This senior technical role focuses on distributed machine learning infrastructure, GPU performance, model inference, and scalable platforms that support advanced AI research and deployment.

Job Overview

  • Company: micro1
  • Job Title: Member of Technical Staff, Model Training (Infra & Platform)
  • Openings: 1
  • Job Type: Full-time
  • Location: Remote
  • Hourly Pay Listed on Job Card: $7–$8 per hour
  • Key Areas: Distributed ML Training, GPU Infrastructure, Distributed Systems, Platform Architecture

Important Compensation Notice

The job card lists $7–$8 per hour, while the job description states a national annual base salary range of $200,000–$320,000, plus potential equity and performance-based bonuses.

These figures conflict. The correct compensation for this specific role has not been independently verified. Applicants should confirm the applicable salary and employment terms directly with micro1 before applying.

About the Role

In this role, you will take technical ownership of distributed systems and platforms that help AI researchers train advanced models, run 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. Your architectural decisions will help improve research speed, infrastructure reliability, and model performance.

Key Responsibilities

  • Architect and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD).
  • Design distributed pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
  • Optimize GPU utilization, memory management, communication, storage, and distributed execution.
  • Build and improve agent platform components, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
  • Develop experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability systems.
  • Identify infrastructure bottlenecks and reduce experiment turnaround time.
  • Establish technical standards and architectural patterns for research and engineering teams.
  • Lead engineering initiatives from system design to production operations and mentor other engineers.

Required Skills and Qualifications

  • Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
  • Extensive experience designing and operating scalable backend systems or distributed computing platforms.
  • Proficiency in Python and at least one systems-oriented language such as C++, Rust, or Go.
  • Hands-on experience with GPU-based ML infrastructure, distributed training, model inference, or large-scale compute orchestration.
  • Understanding of compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
  • Experience designing reliable APIs, services, and infrastructure components.
  • Demonstrated technical ownership and experience leading engineering initiatives.
  • Ability to work effectively in fast-moving research environments.

Preferred Qualifications

  • Experience with PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar distributed ML technologies.
  • Familiarity with vLLM, TensorRT-LLM, or other inference optimization frameworks.
  • Experience building reinforcement learning infrastructure or distributed agent execution systems.
  • Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
  • Experience with Kubernetes, container orchestration, cluster scheduling, and cloud GPU infrastructure.
  • Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation.
  • Open-source contributions to distributed computing or high-performance ML systems.

Compensation and Benefits

The supplied job description states a national annual base salary range of $200,000–$320,000. It also mentions equity compensation, possible performance-based bonuses, health-insurance premium reimbursement of up to 100%, paid time off, a 401(k) plan with company matching, and other benefits subject to company policies.

Important: The annual salary stated in the description conflicts with the hourly amount on the job card. Confirm the correct compensation, location eligibility, and benefits directly with the employer.

How to Apply

Visit the official micro1 website and search for the exact title: Member of Technical Staff, Model Training (Infra & Platform).

Visit the official micro1 website

This is the general company website, not a verified direct application link for this specific role.

Equal Opportunity and Accessibility

micro1 states that it is an equal opportunity employer. Its hiring process uses AI tools to assist candidate screening and assessment, designed to complement rather than replace human decision-making.

For assistance or reasonable accommodation during the application process, contact support@micro1.ai.

Disclaimer

This post is based on the supplied job description and has not been independently verified. Pay, availability, eligibility, and benefits may change. Review the official listing before applying. AQZORA Jobs does not guarantee selection or employment.

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