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Senior Software Engineer for Machine Learning Infrastructure

2 months ago

Bodega Bay, California, United States Yurts Full time
Job Description

**About Yurts**

At Yurts, we are at the forefront of driving innovation in machine learning (ML) infrastructure to deliver cutting-edge solutions. Our mission is to create robust ML infrastructures leveraging Kubernetes and containerization technologies, optimizing ML inference for high-performance and low-latency models.

**Key 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.

**Requirements**

  • 5+ years of relevant experience in ML infrastructure development.
  • 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.

**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

**What We Offer**

We offer a competitive salary range of $180,000—$220,000 USD and a dynamic work environment that fosters innovation and growth.