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AI/ML Infrastructure Engineer

Posted 2024-11-20

View full description

💸 Salary: 120000 - 150000 USD per year

🔍 Industry: Cloud Computing

🏢 Company: Vultr

Requirements:
  • Hands-on experience with high-performance NVIDIA GPUs.
  • In-depth experience automating bare metal internals including BIOS and firmware.
  • Experience with rail optimization across clusters and architectures.
  • Proficiency in Linux, package management, and device drivers.
  • Experience with commercial firmware.
  • Skills in Python, Bash, and PHP.
  • Experience with Machine Learning software.
Responsibilities:
  • Developing and maintaining infrastructure in bare metal and containerized environments.
  • Building scalable and supportable GPU clusters in collaboration with the networking team.
  • Ensuring consistent and reliable provisioning of GPU infrastructure for a positive customer experience.
  • Creating and maintaining test automation for fast, reliable provisioning of GPU products.
  • Benchmarking and performance testing of GPU systems to identify and resolve limitations.
  • Coordinating with vendors for drivers, support, and hardware issues.
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🔥 Sr. AI/ML Infrastructure Engineer
Posted 2024-11-21

📍 United States

🔍 Mental health care technology

  • 5+ years of industry experience building production-level ML platforms and infrastructure.
  • Ability to write high-quality code in Python, Java, or Scala.
  • Experience building production-ready RESTful APIs and scaling platforms for large user bases.
  • Desire to own parts of an ML Platform with understanding of ML models and principles.
  • Experience with containers and deploying applications to Kubernetes.
  • Familiarity with LLMs and building infrastructure for LLM applications.
  • Experience with relational and low-latency databases.
  • Experience in transforming data in batch and streaming contexts.
  • Ability to manage large projects from scoping to delivery.
  • Strong communication and organizational skills, with the ability to simplify complex problems.

  • Be part of a team building scalable infrastructure for training, evaluating, deploying, performing inference, and monitoring ML models.
  • Build, deploy, and maintain generative AI services and applications.
  • Create data systems to collect, clean, label, and store data used for model features.
  • Deploy and manage applications in Kubernetes clusters.
  • Collaborate with Machine Learning engineers to support experimentation platforms and training frameworks.
  • Work with stakeholders to address requirements for ML infrastructure.

AWSPythonJavaKubeflowKubernetesMachine LearningMLFlowPyTorchCommunication SkillsRESTful APIsOrganizational skills

Posted 2024-11-21
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