このポジションについて

As an AI Infrastructure Engineer, you will build and optimize the systems that power Pokee’s RL-trained AI agents—from scalable training pipelines to high-performance inference serving across cloud and on-device deployments. You’ll ensure that our research breakthroughs translate into production infrastructure that enterprises can rely on.

担当していただくこと

Design, build, and maintain scalable training and inference infrastructure for RL-based AI agent models
Optimize model serving for latency, throughput, and cost across cloud (AWS, GCP) and on-premise/on-device environments
Develop and manage CI/CD pipelines, experiment tracking, and model versioning systems
Implement efficient data pipelines for training data collection, preprocessing, and reward signal computation
Collaborate with research scientists to productionize new algorithms and model architectures
Ensure infrastructure meets enterprise requirements for reliability, security, and compliance (SOC 2, data residency)

求める人物像

必須要件

3+ years of experience in ML infrastructure, ML platform engineering, or a related systems role
Strong proficiency in Python and systems-level languages (Rust, C++, or Go)
Hands-on experience with ML serving frameworks (vLLM, TensorRT, Triton, ONNX Runtime, or similar)
Experience with container orchestration (Kubernetes, Docker) and cloud infrastructure (AWS or GCP)
Solid understanding of GPU computing, distributed systems, and performance profiling
Familiarity with ML experiment tracking and pipeline orchestration tools (MLflow, Weights & Biases, Airflow, or similar)

歓迎要件

Experience with on-device / edge inference optimization (GGUF quantization, TensorRT-LLM, CoreML, QNN)
Familiarity with on-premise GPU deployments (NVIDIA DGX, Dell PowerEdge, Lenovo ThinkStation)
Experience supporting RL training loops or online learning systems in production
Background in enterprise software with knowledge of security and compliance frameworks
Contributions to open-source ML infrastructure projects

こんな方を求めています

You want to join a small, elite team solving one of the hardest problems in AI—building agents that actually work in the real world. You’ll have direct impact on the product, access to cutting-edge research, and the opportunity to shape the future of enterprise AI from the ground up.

このポジションについて

担当していただくこと

Design, build, and maintain scalable training and inference infrastructure for RL-based AI agent models
Optimize model serving for latency, throughput, and cost across cloud (AWS, GCP) and on-premise/on-device environments
Develop and manage CI/CD pipelines, experiment tracking, and model versioning systems
Implement efficient data pipelines for training data collection, preprocessing, and reward signal computation
Collaborate with research scientists to productionize new algorithms and model architectures
Ensure infrastructure meets enterprise requirements for reliability, security, and compliance (SOC 2, data residency)

求める人物像

必須要件

3+ years of experience in ML infrastructure, ML platform engineering, or a related systems role
Strong proficiency in Python and systems-level languages (Rust, C++, or Go)
Hands-on experience with ML serving frameworks (vLLM, TensorRT, Triton, ONNX Runtime, or similar)
Experience with container orchestration (Kubernetes, Docker) and cloud infrastructure (AWS or GCP)
Solid understanding of GPU computing, distributed systems, and performance profiling
Familiarity with ML experiment tracking and pipeline orchestration tools (MLflow, Weights & Biases, Airflow, or similar)

歓迎要件

Experience with on-device / edge inference optimization (GGUF quantization, TensorRT-LLM, CoreML, QNN)
Familiarity with on-premise GPU deployments (NVIDIA DGX, Dell PowerEdge, Lenovo ThinkStation)
Experience supporting RL training loops or online learning systems in production
Background in enterprise software with knowledge of security and compliance frameworks
Contributions to open-source ML infrastructure projects

AI Infrastructure Engineer

このポジションについて

担当していただくこと

求める人物像

必須要件

歓迎要件

こんな方を求めています