Title: Senior ML Engineer (Google Cloud Platform)
Location: Remote (USA)
Note: Need candidates with 10+ years of experience. Google Cloud Platform cloud experience is mandatory. Client is looking for ML engineers, not GenAI/Agentic AI Engineers or MLOps Engineers
Job Description:
You will own the end-to-end ML model lifecycle from post-training through production - everything after the researchers hand off a trained model. This is not a research role. You are the engineer who takes models and makes them real: benchmarked, deployed, monitored, and integrated into live production applications. You will work directly with ML researchers, production engineers, and platform teams in a fast-moving hybrid cloud environment.
Technical Stack:
10+ experience
Primary platform: Google Cloud Platform (inference, deployment automation, experimentation, sampling)
Production integration: Java-based streaming pipelines (model integration layer)
Infrastructure: Hybrid - on-premise streaming + Google Cloud Platform serving stacks
Distributed systems: Working knowledge required for debugging and end-to-end testing (not deep expertise)
Machine Learning frameworks: TensorFlow, PyTorch, JAX or similar
Must-Have:
Strong foundation in ML inference, deployment, and quality testing
Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks - this is the single most important trait
End-to-end problem-solving mindset - can own a problem from model handoff to user-facing behavior
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 (model integration happens in Java; deep expertise not required)
Familiarity with streaming data architectures
Experience in hybrid cloud/on-prem environments.
What You Will Do:
Inference & Deployment
Evaluate and benchmark new ML inference frameworks to guide production decisions
Deploy models to Google Cloud Platform and integrate them into production applications and Java-based streaming pipelines
Own deployment automation end-to-end - from model handoff through live serving
Monitor how models behave in production for real end-users.
Performance & Quality
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 - from inference layer down to streaming pipelines.
Cross-functional Collaboration
Partner with ML researchers to provide benchmarking feedback and guide inference decisions - requires enough core ML knowledge to have a meaningful technical handshake
Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + Google Cloud Platform) infrastructure.
Education:
Bachelor's or Master's degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.
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