Summary
✨ AI‑Generated
Join a team building production-grade infrastructure for reinforcement-learning-based AI agents. You will design scalable training systems, optimize high-performance model serving across cloud and on-device environments, and establish robust CI/CD, experiment tracking, model versioning, and data pipelines.
Highlights
Build scalable AI infrastructure spanning training and inference, with opportunities to optimize performance, reliability, and cost across cloud and edge environments.
Description
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AI Infrastructure Engineer
Preferred
Engineering Remote (US/Singapore Preferred) Full-time
Build and optimize the systems that power Pokee's RL-trained AI agents—from scalable training pipelines to high-performance inference serving.
About The Role
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.
What You’ll Do
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)
What We’re Looking For
Required
3+ years of experience in ML infrastructure, ML platform engineering, or a related systems roleStrong 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 profilingFamiliarity with ML experiment tracking and pipeline orchestration tools (MLflow, Weights & Biases, Airflow, or similar)
Bonus Points
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 productionBackground in enterprise software with knowledge of security and compliance frameworksContributions to open-source ML infrastructure projects
Who You Are
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.
Apply for AI Infrastructure Engineer
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