Description

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 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 + GCP) 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-prem streaming + GCP 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 GCP 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. Strong system design skills expected at both levels. Tech-lead caliber — able to connect the dots across stacks independently

Key Skills

Education

Bachelor's degree