Senior Machine Learning Engineer
About ExaCare AI
ExaCare AI is a leading healthtech company dedicated to creating the AI operating system for post-acute care. Our platform streamlines referral sources, admissions workflows, and reimbursement systems, enabling teams to efficiently review documentation, evaluate patients, and make confident decisions. Currently powering over 2,000 post-acute facilities, ExaCare AI has received a $30M Series A investment from Insight Partners, bringing together top talent to revolutionize healthcare.
About The Role
We are seeking a Senior Machine Learning Engineer, MLOps to drive the operationalization and scaling of our machine learning systems. This role focuses on developing the necessary workflows, infrastructure, and processes to ensure ML moves from research to reliable production systems. You will collaborate with research-oriented ML teammates to produce scalable, maintainable, and cost-effective systems.
What You’ll Do
- Build and maintain workflows and infrastructure for the entire ML lifecycle
- Partner with researchers and ML practitioners to productionize models and accelerate development cycles
- Design, build, and enhance data pipelines and training pipelines
- Optimize data processing, annotation workflows, and ML system efficiency
- Deploy and maintain background systems supporting model training and inference
- Create and refine tools and processes for monitoring model performance, system reliability, and operational health
- Enhance scalability, observability, and reproducibility of ML systems
- Optimize ML infrastructure for speed, reliability, and cost-effectiveness
- Identify and address bottlenecks in the ML workflow, automating or streamlining manual processes
- Establish best practices for ML operations, deployment, and system performance
What You’ll Bring
- Significant experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering
- End-to-end experience supporting ML systems, from model handoff to deployment and monitoring
- Strong background in building and managing data pipelines, training pipelines, or similar production workflows
- Proven ability to work closely with researchers, data scientists, or ML practitioners to productionize models
- Strong software engineering fundamentals and experience in building production systems
- Experience with monitoring, debugging, and improving production ML or data systems
- A track record of enhancing reliability, scalability, speed, and cost efficiency in ML systems
- Comfortable operating in a fast-paced, startup-like environment with a strong sense of ownership
Benefits + Perks
- Competitive compensation range: $200,000–$240,000+ USD base salary
- Flexible PTO policy
- Comprehensive medical, dental, and vision coverage
- Startup culture with regular off-site events
- A high-performing team, including ex-Amazon engineers and alumni from top firms like Bain, BCG, Goldman Sachs, and more
- An opportunity to contribute to a mission-driven company focused on improving healthcare outcomes