Senior Machine Learning Engineer

Quantum Technologies. LLC; San Jose (CA); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Quantum Technologies. LLC is looking for a Machine Learning Engineer to own the end-to-end ML model lifecycle from post-training through production, including benchmarking, deployment, monitoring, and integration into live production applications.

You will work with ML researchers, production engineers, and platform teams in a fast-moving hybrid cloud environment on inference, deployment automation, and production integration with Java-based streaming pipelines.

Qualifications

• Strong foundation in ML inference, deployment, and quality testing.
• 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.

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
• 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

Skills

ML inference

Deployment

Quality testing

GCP

End-to-end solving

Tools

TensorFlow

PyTorch

JAX

Java

Job description

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.