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

What You'll Do8

• Architect and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD) across 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.
• Build high-performance infrastructure for model training and inference, focusing on GPU utilization, memory management, communication, storage, and distributed execution.
• Develop and evolve the core architecture of micro1's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
• Create systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve reproducibility and operational reliability.
• Identify and remove infrastructure bottlenecks to reduce experiment turnaround time and support increasingly complex training workloads.
• Define technical standards, platform interfaces, and architectural patterns that enable 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 challenging components.

Requirements8

• Strong computer science fundamentals, especially 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 such as C++, Rust, or Go.
• Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
• Solid 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: 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.

Who Should Apply

Engineers with a strong distributed systems background and hands-on GPU infrastructure experience who want to own platform architecture for model training. You have built and operated scalable backend systems, are fluent in Python and a systems language, and have worked with distributed training or inference at scale. You thrive in research environments where requirements shift and you can lead technical initiatives while mentoring others.

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