SS - ML ENGINEER ROLE

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ML engineer

Location – remote – PST hours

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Job Description 1: Machine Learning Engineer (IC4/IC5)

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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.

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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

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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)

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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

Shekar

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