ML Infrastructure Engineer
March 16
🔄 Hybrid – Bay Area
Description
• Develop Cloud Data Infrastructure System to support and enable Assured AI for Autonomy • Architect and deploy cloud and on-prem ML training and evaluation infrastructure • Own data management pipelines, from ingestion and storage to model training and evaluation • Change model training code to take advantage of proposed data storage techniques • Evaluate and implement methods, software, and hardware for model deployment • Develop systems and processes to improve model transition from research to production • Participate in model design, research, and requirements setting • Own and deliver projects end-to-end
Requirements
• Experience in architecting and implementing data engineering solutions for a small engineering team / product • 2+ years of software engineering experience in ML Infrastructure, Data Engineering, Platform Engineering, or Distributed Systems • Production ML experience in model conversion and optimization, model deployment on specialized hardware, or model monitoring and MLOps • Ability to programmatically access cloud services using Python, NodeJS, or equivalent • Knowledge of or experience with data management solutions such as Workflow orchestration pipelines or Managed large-scale data processing systems • End-to-end ML pipelines experience • Extra Credit: Experience with Terraform or similar IAC solution, Robotics background, C++, ROS, or Google Cloud Platform familiarity
Benefits
• Competitive salary between $150,000 - $200,000 a year • Equal Opportunity Employer
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