Platform ML Engineer

Neurex AI; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Neurex AI is seeking an ML infrastructure engineer to design and operate the systems that support AI applications in healthcare. You will handle training data, experiments, serving, monitoring and performance tuning across production environments.

You will build scalable training/inference infrastructure, real-time and batch serving, data pipelines, experiment tracking and model versioning, with a focus on cost and latency. Remote-friendly in the US.

Qualifications

• Strong foundation in ML systems and infrastructure.
• Experience with model serving tools such as TensorFlow Serving or Triton.
• Familiarity with AWS or GCP and with Kubernetes.
• Experience building data pipelines and working with distributed systems.
• Knowledge of MLOps practices and tooling.
• Clear communication and high engineering standards.

Responsibilities

• Design scalable infrastructure for model training and inference.
• Build serving systems for low-latency applications, including real-time and batch workloads.
• Create data pipelines and feature management systems for training and evaluation.
• Add experiment tracking, model versioning, monitoring, and drift detection.
• Improve model serving for cost and latency needs.
• Partner with research and product teams to deploy new models.

Skills

ML systems

Model serving

Cloud platforms

Kubernetes

Data pipelines

MLOps

Communication

Tools

TensorFlow Serving

Triton

AWS

GCP

Kubernetes

Job description

Role overview

Build and operate the machine learning infrastructure that supports AI applications in healthcare environments. This engineering role covers the full model lifecycle, from training data and experiments through serving, monitoring, and performance tuning in production.

Responsibilities

• Design scalable infrastructure for model training and inference.
• Build serving systems for low-latency applications, including both real-time and batch workloads.
• Create data pipelines and feature management systems for training and evaluation.
• Add experiment tracking, model versioning, monitoring, and drift detection.
• Improve model serving for cost and latency needs.
• Partner with research and product teams to deploy new models.

Requirements

• Strong foundation in machine learning systems and infrastructure.
• Experience with model serving tools such as TensorFlow Serving or Triton.
• Familiarity with cloud platforms such as AWS or GCP and with Kubernetes.
• Experience building data pipelines and working with distributed systems.
• Knowledge of MLOps practices and tooling.
• Clear communication and high engineering standards.

Benefits and work setup

• Full-time role based in New Jersey or remote.
• Opportunity to build infrastructure for AI used in real healthcare workflows, with substantial ownership and autonomy.