Production ML Engineer: End-to-End Deployment & Monitoring

Ethereum Technologies; San Jose (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Ethereum Technologies in San Jose, CA, seeks a Machine Learning Engineer to own the end-to-end ML model lifecycle—from post-training to production. You will benchmark frameworks, deploy models on GCP, and integrate with Java-based streaming pipelines within a fast-moving hybrid cloud environment.

You will monitor production behavior, perform benchmarking and quality testing, and collaborate with ML researchers and platform teams to drive reliable, scalable AI applications.

Qualifications

• Strong foundation in ML inference, deployment, and quality testing.
• Ability to ramp up quickly on unfamiliar tech stacks.
• End-to-end problem-solving mindset from model handoff to user-facing behavior.
• Core ML knowledge to benchmark models and collaborate with researchers.
• Experience deploying models in cloud environments, ideally GCP.

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.
• Debug issues across the full stack — from inference layer down to streaming pipelines.

Skills

ML inference

Deployment

Quality testing

Cloud (GCP)

End-to-end problem-solving

Research collaboration

Tools

TensorFlow

PyTorch

JAX

Java

Streaming pipelines

Job description

Ethereum Technologies in San Jose, CA, seeks a Machine Learning Engineer to own the end-to-end ML model lifecycle—from post-training to production. You will benchmark frameworks, deploy models on GCP, and integrate with Java-based streaming pipelines within a fast-moving hybrid cloud environment.

You will monitor production behavior, perform benchmarking and quality testing, and collaborate with ML researchers and platform teams to drive reliable, scalable AI applications.