Senior ML Deployment Engineer - GCP & Hybrid Cloud

Machine Learning Technologies, LLC; San Jose (CA); Híbrido; USD 120.000 - 180.000; Tempo integral

Resumo da vaga

Machine Learning Technologies, LLC is hiring a Machine Learning Engineer to own the end-to-end ML model lifecycle from post-training through production. You will benchmark, deploy, monitor, and integrate models into live production applications in a fast-moving hybrid cloud environment.

The role collaborates with ML researchers and platform teams, deploying in GCP with Java-based pipelines, and shaping performance through ongoing testing and benchmarking.

Qualificações

• Strong foundation in ML inference, deployment, and quality testing.
• Ramp up quickly on new and 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.

Responsabilidades

• 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 performance and quality.
• Design and execute benchmarking, performance testing, and quality testing on ML models.
• Perform model sampling to support quality evaluation and researcher feedback loops.
• Collaborate with ML researchers to provide benchmarking feedback and guide inference decisions.
• Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure.

Conhecimentos

ML inference

Deployment

Quality testing

End-to-end

GCP experience

Java

Ferramentas

TensorFlow

PyTorch

JAX

GCP

Java

Descrição da vaga

Machine Learning Technologies, LLC is hiring a Machine Learning Engineer to own the end-to-end ML model lifecycle from post-training through production. You will benchmark, deploy, monitor, and integrate models into live production applications in a fast-moving hybrid cloud environment.

The role collaborates with ML researchers and platform teams, deploying in GCP with Java-based pipelines, and shaping performance through ongoing testing and benchmarking.