Senior Machine Learning Engineer (Inference & Deployment)

About the Role:

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.

What You Need (Must-Haves)

• 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.

GlobalLogic estimates the starting pay range for this role to be performed remotely to be $130,000 to $140,000 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.