Member of Technical Staff, Model Training (infra & platform), Remote- Full TIme

Xperteez Technology • ,

13d • $600,000 - $1,800,000 • Remote

About The Position

Take technical ownership of distributed systems and platforms that enable researchers to train increasingly capable models, run large-scale experiments, and deploy specialized AI systems efficiently. The role works across distributed training, GPU infrastructure, inference optimization, simulation environments, and agent execution frameworks, making architectural decisions that influence research velocity and model performance.

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 decision-making, leading cross-functional initiatives, and delivering systems other engineers depend on.
• Ability to work effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.

Nice To Haves

• 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.

Responsibilities

• 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 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 the core architecture of micro1's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
• Develop systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve reproducibility and operational reliability.
• Identify and eliminate infrastructure bottlenecks to reduce experiment turnaround time and support increasingly complex training workloads.
• Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
• Lead engineering initiatives from system design through implementation and production operations, mentor engineers, and maintain ownership of technically challenging components.

Benefits

• Equity compensation; all employees are eligible.
• Employees may also receive performance-based bonuses, dependent on role and subject to company policies.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed