Senior MLOps Engineer: Production-Grade ML Pipelines

exacare ai; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Flexible PTO

Medical, dental, and vision coverage

Competitive salary and equity

Great startup culture

Off-sites

Job summary

Exacare ai, a health tech startup in New York, seeks a Senior Machine Learning Engineer, MLOps to scale ML systems from research to production. This role focuses on building workflows, infrastructure, and processes for end-to-end ML operations.

You will partner with researchers to productionize models, design data and training pipelines, and improve monitoring, reliability, and cost-efficiency of deployments.

Qualifications

• Several years of experience in machine learning engineering, MLOps, or ML infrastructure.
• Experience supporting ML end-to-end from data to deployment and monitoring.
• Strong software engineering fundamentals for production systems.
• Experience building data pipelines and training pipelines.
• Ability to work with researchers to productionize models.

Responsibilities

• Build and maintain end-to-end ML lifecycles workflows and infrastructure.
• Productionize models with researchers and practitioners for faster iteration.
• Design and improve data pipelines and training pipelines.
• Improve data processing and annotation workflows for ML systems.
• Deploy and maintain background systems for training and inference.
• Build tooling for monitoring model performance and system health.
• Improve scalability, observability, and reproducibility of ML systems.
• Optimize ML infrastructure for speed, reliability, and cost-efficiency.
• Identify bottlenecks and automate manual ML processes.
• Establish best practices around ML operations, deployment.

Skills

ML engineering

MLOps

ML infrastructure

Data engineering

Backend engineering

Job description

Exacare ai, a health tech startup in New York, seeks a Senior Machine Learning Engineer, MLOps to scale ML systems from research to production. This role focuses on building workflows, infrastructure, and processes for end-to-end ML operations.

You will partner with researchers to productionize models, design data and training pipelines, and improve monitoring, reliability, and cost-efficiency of deployments.