AI Research Engineer (Kernel & Inference Optimization) - 100% Remote Worldwide

About the job

As a member of our AI model team, you will drive innovation in model serving and inference architectures for advanced AI systems. Your work will focus on optimizing model deployment and inference strategies to deliver highly responsive, efficient, and scalable performance across real-world applications. You will work on a wide spectrum of systems, ranging from resource-efficient models designed for limited hardware environments to complex, multi-modal architectures that integrate data such as text, images, and audio.

We expect you to have deep expertise in designing and optimizing model serving pipelines and inference frameworks as well as a strong background in advanced model architectures. You will adopt a hands-on, research-driven approach to develop, test, and implement novel serving strategies and inference algorithms. Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments. The ultimate goal is to enable high-throughput, low-latency, low-memory footprint, and scalable AI performance that delivers tangible value in dynamic, real-world scenarios.

Responsibilities

• Design and deploy state-of-the-art model serving architectures that deliver high throughput and low latency while optimizing memory usage.
• Ensure these pipelines run efficiently across diverse environments, including resource-constrained devices and edge platforms.
• Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint.