ML Engineer
Machine Learning Engineer (IC4/IC5)
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.
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 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) What We Are Looking For 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