Senior System Engineer

Description Primary platform: Google Cloud Platform (inference, deployment automation, experimentation, sampling) Production integration: Java-based streaming pipelines (model integration layer) Infrastructure: Hybrid – on‑premise streaming + GCP serving stacks Distributed systems: Working knowledge required for debugging and end‑to‑end testing (not deep expertise) 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 GCP Requirements Perform model sampling to support quality evaluation and researcher feedback loops Debug issues across the full stack – from inference layer down to streaming pipelines 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 + GCP) infrastructure Job Responsibilities Evaluate and benchmark new ML inference frameworks to guide production decisions Deploy models to GCP 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 Design and execute benchmarking, performance testing, and quality testing on ML models Bachelor’s or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field #J-18808-Ljbffr