ML Engineer

About the Role

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.

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

Technical Stack

• 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