Senior Machine Learning Engineer

Machine Learning Technologies, LLC; San Jose (CA); Híbrido; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

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 oferta de emprego

Machine Learning Engineer 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. What You Will Do Inference & Deployment.

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.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.