Data & Machine Learning Engineer

Introduction: The System Engineer will be responsible for working on the primary platform of Google Cloud Platform, deploying models, and integrating them into production applications and Java-based streaming pipelines. They will also be involved in monitoring model behavior in production, benchmarking, and performance testing.

Responsibilities:

• Evaluate and benchmark new ML inference frameworks
• Deploy models to Google Cloud Platform and integrate them into production applications and Java-based streaming pipelines
• Own deployment automation end-to-end
• Monitor model behavior in production for real end-users
• Design and execute benchmarking, performance testing, and quality testing on ML models
• Perform model sampling to support quality evaluation and researcher feedback loops
• Debug issues across the full stack
• Partner with ML researchers to provide benchmarking feedback and guide inference decisions
• Adapt rapidly to non-standard and evolving tech stacks across hybrid infrastructure

Requirements:

• Bachelor''s or Master''s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field
• Strong foundation in ML inference, deployment, and quality testing
• Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks
• End-to-end problem-solving mindset
• Core ML knowledge sufficient to benchmark models and collaborate with researchers
• Experience deploying models in cloud environments, ideally Google Cloud Platform

Good to Have:

• Exposure to Java or JVM-based systems
• Familiarity with streaming data architectures
• Experience in hybrid cloud/on-prem environments

Note: GlobalLogic estimates the starting pay range for this role to be performed remotely to be $120,000 to $130,000 and reflects base salary only. This pay range is provided as a good-faith estimate, and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.