Senior Machine Learning Engineer (GCP)

Title: Senior ML Engineer (GCP)

Location: Remote (USA)

Job Description:

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.

Technical Stack:

• 10+ experience
• 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)
• 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 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.

What You Will Do:

Inference & Deployment

• 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.

Education:

• Bachelor's or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.