Senior ML Engineer - Production Deployment & Benchmarking

Cloud Analytics Technologies, LLC; San Jose (CA); Híbrido; USD 120.000 - 190.000; Jornada completa

Descripción de la vacante

Cloud Analytics Technologies, LLC is seeking 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.

Responsibilities include evaluating new ML inference frameworks, deploying models to GCP with Java-based pipelines, and owning end-to-end deployment automation while collaborating with ML researchers and platform

Formación

• Strong foundation in ML inference, deployment and quality testing.
• Demonstrated ability to ramp up quickly on new tech stacks; self-starter.
• 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 real end-users.
• 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.
• Partner with ML researchers to provide benchmarking feedback and guide inference decisions.

Conocimientos

ML inference

Deployment

Cloud experience

Java

Streaming pipelines

Herramientas

TensorFlow

PyTorch

JAX

GCP

Java

Descripción del empleo

Cloud Analytics Technologies, LLC is seeking 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.

Responsibilities include evaluating new ML inference frameworks, deploying models to GCP with Java-based pipelines, and owning end-to-end deployment automation while collaborating with ML researchers and platform