Member of Technical Staff, Model Training (infra & platform)

micro1; United States; Remote; USD 200,000 - 320,000; Full time

Benefits offered by this job

Equity

Health premium reimbursement

401K match

Remote-first

Job summary

micro1 is seeking a Member of Technical Staff, Model Training (infra & platform) to own distributed training platforms and GPU infrastructure for researchers. You will drive architecture decisions across training, inference, and simulation environments, enabling faster experiments and high-performance model deployment.

The role emphasizes technical ownership, cross-functional collaboration, and mentoring while delivering robust, scalable infrastructure for ML workloads in a remote-first setting.

Qualifications

• Strong CS fundamentals in distributed systems, concurrency, networking, and performance engineering.
• Experience designing and operating scalable backend systems or distributed computing platforms.
• Proficiency in Python and a systems language (C++, Rust, or Go).
• Hands-on experience with GPU-based ML infrastructure, distributed training, or large-scale compute orchestration.
• Strong understanding of system performance, memory efficiency, and fault tolerance.
• Experience designing reliable APIs, services, and infra components for complex workflows.
• Demonstrated technical ownership and cross-functional leadership across teams.
• Ability to thrive in fast-moving research environments with evolving requirements.

Responsibilities

• Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), RL, and on-policy distillation for language, multimodal, and robotics workloads.
• Design distributed training and execution pipelines coordinating GPU workers, rollout generation, teacher-model inference, reward computation, and simulations.
• Develop high-performance infra for model training and inference, optimizing GPU utilization and memory management.
• Build and evolve core architecture of micro1's agent ecosystem, including runtimes, interfaces, and APIs.
• Develop robust systems for experiment tracking, versioning, checkpointing, fault recovery, and observability.
• Identify and eliminate bottlenecks to reduce experiment turnaround time for complex workloads.
• Define standards, interfaces, and architecture patterns to support collaboration across teams.
• Lead engineering initiatives from design to production, mentoring engineers while owning challenging components.

Skills

Distributed systems

Concurrency

Python

C++/Rust/Go

GPU ML infra

API design

Tools

PyTorch Distributed

DeepSpeed

Ray

Kubernetes

NCCL

CUDA

Job description

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

1. 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.
2. Design distributed training and execution pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
3. Develop high-performance infrastructure for model training and inference, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
4. Build and evolve the core architecture of micro1's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
5. Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve research reproducibility and operational reliability.
6. Identify and eliminate infrastructure bottlenecks, reducing experiment turnaround time and enabling researchers to run increasingly complex training workloads.
7. Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
8. 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

1. Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
2. Extensive experience designing, building, and operating scalable backend systems or distributed computing platforms.
3. Proficiency in Python and at least one systems-oriented programming language, such as C++, Rust, or Go.
4. Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
5. Strong understanding of system performance, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
6. Experience designing reliable APIs, services, and infrastructure components that support complex engineering or research workflows.
7. Demonstrated technical ownership, including making architectural decisions, leading cross-functional initiatives, and delivering systems that other engineers depend on.
8. Ability to operate effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.

Nice to Have

1. Experience with distributed ML frameworks and technologies such as PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar systems.
2. Familiarity with GPU inference engines and optimization frameworks such as vLLM, TensorRT-LLM, or equivalent technologies.
3. Experience building reinforcement learning infrastructure, rollout orchestration systems, or distributed agent execution environments.
4. Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
5. Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.
6. Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation systems.
7. 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. micro1 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.

micro1 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 [e-mail].

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.