Software Engineer

Summary

Join our dynamic team at Yurts as a Senior Software Engineer for ML Infrastructure to lead the development of state-of-the-art ML deployment systems.

Requirements

• 5+ years of relevant experience in ML infrastructure development
• 1+ years of professional development experience with Rust
• Proven track record of extensive experience with Kubernetes and containerization technologies, demonstrating a strong ability to deploy and manage distributed systems at scale
• Hands-on experience in optimizing ML inference using CUDA and GPU-accelerated computing, achieving significant performance gains for complex ML models
• Deep understanding of DevOps practices and experience implementing CI/CD pipelines, ensuring a smooth and efficient development and deployment process
• Demonstrated expertise in model scheduling and autoscaling techniques, allowing dynamic resource allocation to meet varying inference workloads
• Strong architectural and software development skills, with a passion for crafting elegant and efficient solutions that push the boundaries of ML infrastructure capabilities

Responsibilities

• Design, deploy, and maintain robust ML infrastructures using Kubernetes and containerization technologies to enable seamless and scalable deployment of machine learning models
• Utilize your deep knowledge of CUDA and GPU-accelerated computing to optimize ML inference, delivering high-performance and low-latency models for demanding applications
• Champion DevOps practices and streamline CI/CD pipelines to enhance the software development lifecycle and increase deployment efficiency
• Lead efforts to develop and implement model scheduling and autoscaling strategies, dynamically allocating resources based on real-time inference demands to ensure optimal resource utilization
• Collaborate with cross-functional teams, taking an active role in architectural discussions and hands-on development to drive innovation and push the boundaries of ML infrastructures

Preferred Qualifications

• Experience in deploying and managing machine learning models in cloud environments such as AWS, GCP, or Azure
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch, or ONNX, and their integration with inference engines