Machine Learning Engineer, MLOps

exacare ai; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Exacare ai is seeking a Machine Learning Engineer to operationalize and scale machine learning systems. This engineering-focused role centers on building workflows, infrastructure, and processes that move ML from research into reliable production systems.

You will partner with research-oriented ML teammates to turn their work into scalable, maintainable, and cost-effective production solutions, including data/training pipelines and deployment workflows.

Qualifications

• 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.

Responsibilities

• 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 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.

Skills

ML engineering

MLOps

Data pipelines

Backend | Systems

Python

Docker

Kubernetes

Tools

Python

Docker

Kubernetes

TensorFlow/PyTorch

Job description

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 2,000 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

Only the best belong here

We are unapologetic about talent. This should be the best team you have ever been on. Protecting that standard is how we honor each other’s time, ambition, and craft.

We work even harder to keep our partners than we did to earn them initially

The work does not stop when a customer first onboards to our platform. It deepens over time. We partner with operators, listening and learning about real problems, and translate that into solutions that help them succeed in practice. We earn trust through consistent delivery.

We keep the patient downstream of every decision

At the end of the day, this is about the patient. We get there by deeply respecting and reflecting on our purpose: to develop software that aids teams in delivering better care.

Raise the bar on ownership

We grow because people here go beyond the minimum. We invest extra effort, care, and ownership into what we build.

The world is moving fast. We move faster.

This is a race. We work hard, we move early, and we stay ahead of problems and competitors. If we slow down, someone else will pass us.

We say what’s true, early, and we keep communication direct and clean so the team can move.

Bring good vibes and win together

We win as a team. We bring energy, support each other, and make the workplace somewhere people are excited to show up.

If this sounds like you, we'd love to have a chat!