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

micro1; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

micro1 is seeking a senior engineer to take technical ownership of distributed ML training infrastructure and GPU-optimized systems across large-scale experiments and model deployments. You will shape architecture, coordinate across research and engineering teams, and drive reliability and performance for SFT, RL, and related workloads.

The role emphasizes leading critical engineering initiatives, mentoring engineers, and delivering scalable platform components that enable researchers to push

Qualifications

• Distributed ML training infrastructure
• GPU performance & inference optimization
• Distributed systems & platform architecture
• Technical leadership & system ownership

Skills

Distributed ML Training Infrastructure

GPU Performance & Inference Optim

Distributed Systems & Platform Arch

Technical Leadership & System Ow

Job description

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. 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.

Required skills:

• Distributed ML Training Infrastructure
• GPU Performance & Inference Optimization
• Distributed Systems & Platform Architecture
• Technical Leadership & System Ownership