ML Infrastructure Engineer

Job summary

As an ML Infrastructure Engineer, you will design and build GPU compute infrastructure and collaborate with ML teams to integrate models across the stack.

Qualifications

• Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or equivalent work experience
• 2+ years of experience in high traffic or large-scale production environments
• 2+ years experience with ML platforms and training infrastructure
• Strong proficiency with Python and experience with C++ or Rust

Responsibilities

• Designing, building, and scaling GPU compute infrastructure
• Developing data pipelines and integrating large-scale data
• Collaborating with ML teams to productionize models
• Ensuring scalability, reliability, and efficiency of ML systems
• Working across the full stack to solve complex problems
• Mentoring junior engineers

Skills

Relocation