Hybrid ML Infrastructure Engineer

at Gridmatic

• Job title: ML Infrastructure Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $174,000 - $231,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: PyTorch, Ray, SQL
• Location requirements: Hybrid, Cupertino, United States

About the role

• Machine Learning Infrastructure Engineer optimizing scalable infrastructure for ML at Gridmatic startup. Focus on machine learning, distributed systems, and renewable energy transformation.

Responsibilities

• Own and optimize scalable, robust distributed infrastructure for ML training
• Optimize throughput and cost across clusters and clouds
• Improve efficiencies by optimizing latency and memory consumption
• Help define long-term vision for ML platform
• Mentor junior engineers and contribute to team culture

Requirements

• 3+ years of experience in machine learning and software engineering
• Strong understanding of codebases and ability to write scalable code
• Experience in researching and implementing deep learning models
• Knowledge of GPU clusters and core libraries (PyTorch, PyTorch Lightning, Ray)
• Familiarity with large-scale data storage (Zarr, SQL, feature stores)
• Independence and ownership in engineering robust systems
• Enthusiasm for renewable energy and a willingness to learn