Machine Learning Engineer

ExaCare Inc; New York (NY); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Competitive salary and equity

Flexible PTO

Medical, dental, and vision coverage

Great startup culture

Job summary

ExaCare Inc is seeking a Machine Learning Engineer in New York to operationalize and scale machine learning systems. This engineering-focused role will involve building workflows, infrastructures, and processes to enhance ML system reliability.

The ideal candidate will have 3+ years of experience in MLOps, strong software engineering skills, and a passion for improving ML operations. Join a high-achieving team and be part of the transformation in healthcare with flexible PTO and competitive benefits.

Qualifications

• 3+ years of experience in machine learning engineering or MLOps.
• Strong software engineering fundamentals.
• Experience with monitoring and debugging production ML systems.

Responsibilities

• Build and maintain workflows and infrastructure supporting ML lifecycle.
• Partner with researchers to productionize models.
• Design and improve data and training pipelines.

Skills

Machine learning engineering

MLOps

Data engineering

Backend/platform engineering

Production workflows

Job description

Machine Learning Engineer

ExaCare Inc – New York, New York, United States

About this position

ExaCare AI is a leading health tech company on a mission to build the AI operating system for post-acute care. Our platform turns messy, unstructured referral packets into clear clinical insights and next steps, so teams can make faster, safer placement decisions with less administrative burden. Today, ExaCare AI powers more than 2000 facilities, and is growing rapidly.

We recently raised a $30M Series A led by Insight Partners, and are bringing world-class talent together to transform healthcare. If you like building, learning, and want to make a real impact, come join us!

About the Role

We are looking for a Machine Learning Engineer, MLOps to help operationalize and scale our machine learning systems. This is an engineering-focused role centered on building the workflows, infrastructure, and processes that enable ML to move from research into reliable production systems.

You will partner closely with research-oriented ML teammates and help turn their work into scalable, maintainable, and cost-effective production systems. This includes building and improving data pipelines, training pipelines, deployment workflows, monitoring systems, and supporting infrastructure that allow the team to move faster and operate ML systems with confidence.

This is not a research-first role. It is best suited for someone who is excited by the systems, tooling, and operational side of machine learning.

What You’ll Do

• Build and maintain the workflows and infrastructure that support the end-to-end ML lifecycle
• Partner with researchers and ML practitioners to productionize models and enable faster iteration
• Design, build, and improve data pipelines and training pipelines
• Improve data processing, annotation workflows, and ML system efficiency
• Deploy and maintain the background systems that support model training and inference
• Build tooling and processes for monitoring model performance, system reliability, and operational health
• Improve the scalability, observability, and reproducibility of ML systems
• Optimize ML infrastructure for speed, reliability, and cost-efficiency
• Identify bottlenecks in the ML workflow and automate or streamline manual processes
• Help establish best practices around ML operations, deployment, and system performance

What You’ll Bring

• Proven (3+ years) of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments
• Experience supporting ML systems end to end, from model handoff through deployment and monitoring
• Strong experience building and owning data pipelines, training pipelines, or other production workflows that support ML
• Experience working closely with researchers, data scientists, or ML practitioners to productionize models
• Strong software engineering fundamentals and experience building production systems
• Experience with monitoring, debugging, and improving production ML or data systems
• A track record of improving reliability, scalability, speed, and/or cost efficiency in ML systems
• Comfort operating in a fast-moving, startup-style environment with a high degree of ownership
• Competitive salary and equity in a high-growth startup
• Flexible PTO, take what you need
• Medical, dental, and vision coverage
• Great startup culture, including company off-sites
• High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, Goldman Sachs, and more