Member of Technical Staff, Model Training (Infrastructure & Platform)
M
micro1AI infrastructure
Source API remote eligibility restrictions: United StatesFull-Time
SalaryUSD 600000 - 1800000 / year
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Job Details
- Required Skills
- PythonKubernetesC++GoRustDistributed Systems
Requirements
- 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 for complex engineering or research workflows.
- Demonstrated technical ownership, including architectural decisions, cross-functional initiatives, and delivery of systems used by other engineers.
- Ability to work effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.
- Nice to have: experience with PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar distributed ML systems.
- Nice to have: familiarity with vLLM, TensorRT-LLM, or equivalent GPU inference optimization frameworks.
- Nice to have: experience with reinforcement learning infrastructure, rollout orchestration, or distributed agent execution environments.
- Nice to have: knowledge of CUDA, GPU kernel optimization, NCCL, Kubernetes, container orchestration, cluster scheduling, or cloud-based GPU infrastructure.
Responsibilities
- Architect, build, and scale shared infrastructure for supervised fine-tuning, reinforcement learning, and on-policy distillation across language, multimodal, and robotics workloads.
- Design distributed training and execution pipelines coordinating GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
- Develop high-performance model training and inference infrastructure, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
- Build and evolve agent ecosystem architecture, 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 and eliminate infrastructure bottlenecks to reduce experiment turnaround time and support more complex training workloads.
- Define technical standards, platform interfaces, and architectural patterns for research, infrastructure, and applied engineering collaboration.
- Lead engineering initiatives from system design through implementation and production operations, mentoring engineers while retaining ownership of challenging components.
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