ML Infrastructure Engineer

You will build and scale GPU compute infrastructure, training frameworks, experimentation tools, and data pipelines. You will integrate large-scale training and inference systems, work with ML teams to productionize models, ensure platform reliability and efficiency, solve full-stack problems, and mentor junior engineers.

Responsibilities

• Design, build, and scale GPU compute infrastructure, training frameworks, and experimentation tools
• Develop data pipelines and integrate large-scale data, training, and inference systems
• Collaborate with ML teams to productionize models
• Ensure scalability, reliability, and efficiency of machine learning systems
• Solve full-stack problems independently
• Mentor junior engineers

Requirements

• Bachelor's, Master's, postgraduate degree or PhD in computer science, machine learning, another quantitative discipline, or equivalent work experience
• 2+ years of industry experience with high-traffic or large-scale production environments, distributed systems, GPU infrastructure, or deep learning applications
• 2+ years of experience with ML platforms, training infrastructure, or collaboration with modeling engineers and data scientists
• Strong proficiency with Python
• Experience with C++ or Rust

Benefits

• Equity
• Medical coverage
• Vision coverage
• Dental coverage
• 401(k) retirement plan
• Short-term disability insurance
• Long-term disability insurance
• Life insurance
• Discounts and perks