Who is Eleos Health?

Today, more people than ever are speaking publicly about their mental health. Whether it's ourselves, our friends and family or even public figures, taking care of your behavioral health is no longer a taboo, it's vital, and it's only human.

Eleos is on a mission to help deliver the world's most effective behavioral care through data, measurement, and personalization. Or simply put, we want to give clinicians the support they need to do the important work only they can do.

What is this opportunity?

At Eleos, we build a behavioral health CareOps automation platform that transforms therapy conversations into structured insights and clinical documentation. Our system uses advanced ML and LLM technologies to improve care quality, support therapists’ daily workflows, and reduce documentation time by over 50%.

As a Senior ML Infrastructure Engineer, you will design and build the infrastructure that powers our ML and LLM systems in production. You will develop scalable pipelines, systems, and tools that enable data scientists and AI teams to efficiently develop, test, and deploy models.

Working closely with data scientists, engineers, and product teams, you will ensure our ML capabilities are reliable, scalable, and production-ready—helping bring cutting-edge AI to improve mental health care.

This is a unique opportunity to join a startup with a real impact on thousands of people’s wellbeing and mental health, applying cutting-edge AI technologies to solve meaningful human problems.

How will you contribute?

• Design and build infrastructure and backend services supporting ML and LLM systems
• Develop and maintain ML training and deployment pipelines
• Build tooling that enables model experimentation, versioning, and reproducibility
• Implement CI/CD pipelines for ML workflows and model deployment
• Support LLM deployment, prompt management, and optimization pipelines
• Improve reliability, monitoring, and observability of ML systems in production
• Collaborate with data scientists to productionize models and research prototypes
• Ensure secure and compliant handling of sensitive healthcare data

What qualifications and skills will help you be successful?

• 5+ years of industry experience in ML Infrastructure, Backend Engineering, or related fields
• Strong Python experience with production-grade systems
• Experience working with cloud platforms (AWS, GCP, or Azure)
• Experience with containerization technologies (Docker, Kubernetes)
• Experience building CI/CD pipelines for ML systems or backend services
• Experience supporting LLM deployment or ML models in production
• Familiarity with model versioning, experiment tracking, and ML tooling

Some nice to haves are:

• Experience with prompt engineering and prompt management
• Experience with data versioning tools (DVC, Pachyderm, etc.)
• Experience with MLOps platforms (MLflow, Kubeflow, etc.)