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

Micro1; India; A distancia; INR 18.779.000 - 30.047.000; Jornada completa

Descripción de la vacante

micro1 is seeking a Member of Technical Staff, Model Training (infra & platform) to own distributed systems that empower researchers to train advanced models, run large-scale experiments, and deploy AI systems efficiently.

You will design pipelines for SFT/RL/OPD workloads, optimize GPU usage, and build core agent infrastructure. The role requires strong CS fundamentals and hands-on ML infra experience, in a remote-first environment.

Formación

• Strong CS fundamentals 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 language (C++, Rust, Go).
• Hands-on experience with GPU-based ML infrastructure, distributed training, model inference, or large-scale compute orchestration.
• Strong understanding of system performance metrics, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
• Experience designing reliable APIs, services, and infrastructure components for complex engineering or research workflows.
• Demonstrated technical ownership, including architectural decisions and cross-functional leadership.
• Ability to operate in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.

Responsabilidades

• Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD).
• Design distributed training and execution pipelines coordinating GPU workers, rollout generation, teacher-model inference, reward computation, and simulations.
• 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 micro1's agent ecosystem, including runtimes, interfaces, environment integrations, registries, and APIs.
• Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability for reproducibility and reliability.
• Identify and eliminate infrastructure bottlenecks to reduce turnaround times for complex training workloads.
• Define technical standards, platform interfaces, and patterns to support collaboration across teams.
• Lead engineering initiatives from design to production, mentoring engineers while owning challenging components.

Conocimientos

Distributed systems

GPU infrastructure

Python

C++/Rust/Go

APIs & microservices

Research infra ownership

Educación

Bachelor's degree in CS or related field

Herramientas

PyTorch Distributed

DeepSpeed

Ray

Kubernetes

CUDA

TensorRT-LLM

Descripción del empleo

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

• 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 micro1'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. 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.