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Role Overview:

  • Take ownership of ML infrastructure ensuring model uptime and performance as usage scales.
  • Bridge gap between experimentation and production by turning research artifacts into robust services.
  • Shape on-premises MLOps practices and improve engineering across the ML stack.

Responsibilities:

  • Collaborate with data scientists and engineers to build scalable data pipelines and deployment systems.
  • Troubleshoot issues from Linux and Docker to the highest levels of the ML stack.
  • Design and develop MLOps solutions for seamless handover between research and production.

Ideal Candidate:

  • 3+ years of experience in MLOps or full stack Machine Learning.
  • Strong programming skills in Python, Scientific Python Stack, or Cuda.
  • Understanding of MLOps lifecycle and experience with Kubernetes, cloud platforms, CI/CD, and ML frameworks.

Benefits & Perks:

  • Competitive salary and annual bonus, medical coverage, and life insurance.
  • Vacation and leaves of absence including menstrual and flexible leave.
  • Developmental opportunities, hybrid/remote model, tech/WFH stipends, and premium access to Quillbot.

Learneo

Learneo is a platform of builder-driven businesses, including Course Hero, CliffsNotes, LitCharts, Quillbot, Symbolab, and Scribbr, focused on supercharging productivity and learning. The company supports high-growth businesses with centralized corporate operations and has a virtual-first culture with employees across multiple countries.

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