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# Machine Learning Engineer (Inference & Deployment)

- **Company:** GlobalLogic
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $130,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Software Debugging, Distributed Systems, Java Virtual Machine (JVM), Machine Learning, E2e Testing, Data Streaming, Google Cloud, Cloud Platform System, HybridCloud, Information Technology, Deployment Automation, Machine Learning Operations
- **Published:** August 22, 2026
- **Apply:** https://www.dice.com/job-detail/47df0d8f-c610-47a1-a64c-99e1ce11a433

## About the Role

* 10+ years of engineering experience with a strong foundation in ML inference, deployment, and quality testing.
 * Demonstrated ability to learn and adapt rapidly to non-standard or unfamiliar technologies.
 * Hands-on experience deploying models in cloud environments (Google Cloud Platform preferred).
 * Working knowledge of distributed systems sufficient for effective end-to-end testing and debugging.
 * Core ML knowledge to effectively benchmark models and collaborate with researchers.

Good-to-Have:

 * Exposure to Java or JVM-based systems (model integration happens in Java, but deep expertise is not required).
 * Familiarity with streaming data architectures.
 * Experience operating in hybrid cloud and on-premises environments.

Education: Bachelor''s or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.

## Description

We are looking for a highly adaptable Machine Learning Engineer to bridge the gap between ML research and live production. In this role, you will own the end-to-end inference and deployment lifecycle, integrating models into a hybrid infrastructure consisting of Google Cloud Platform (Google Cloud Platform) serving stacks and on-premises Java-based streaming pipelines.

The single most important trait for this role is adaptability-you must be able to ramp up quickly on unfamiliar, evolving tech stacks while maintaining a strong problem-solving mindset from model handoff to user-facing behavior.

What You Will Do

 * Deploy & Automate: Own deployment automation from model handoff to live serving on Google Cloud Platform, successfully integrating models into Java-based streaming pipelines.
 * Benchmark & Evaluate: Assess new ML inference frameworks, conduct performance/quality testing, and perform model sampling to support researcher feedback loops.
 * Monitor & Debug: Track real-world model behavior and troubleshoot issues across the full distributed stack.
 * Collaborate: Partner closely with ML researchers to guide inference decisions, requiring enough core ML knowledge to establish a strong technical handshake.


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