About the Role
We’re a team of passionate, forward-thinking professionals eager to take on the challenge of the mental health crisis and play a formative role in providing life-saving solutions. If you’re inspired by our mission and energized by the opportunity to increase access to mental healthcare and impact millions of lives in a profound way, apply today.
At Charlie Health, we are passionate about transforming the mental healthcare system, and our tech team is at the heart of building solutions that can change lives. We are looking for an experienced Machine Learning Engineer who is excited about operationalizing ML models that power meaningful outcomes for patients. You will play a key role in scaling our data science efforts and ensuring the infrastructure supports our production systems efficiently.
Responsibilities
- Operationalization and Scalability of ML models: Design and implement strategies for deploying and scaling machine learning models in a production environment.
- Model Deployment: Work closely with data scientists and engineers to deploy machine learning models into production systems, ensuring models are stable and performant.
- Model and Software Development: Build and deploy APIs for machine learning models, ensuring seamless integration with production systems. Develop internal ML tools to streamline model lifecycle management.
- Automation: Automate data pipelines, model training processes, and monitoring tasks to optimize operational efficiency and reduce manual intervention.
- Infrastructure Management: Maintain and optimize the infrastructure (cloud, servers, containers) that supports the deployment and execution of machine learning models, ensuring reliability and scalability.
- Model Monitoring: Continuously monitor models in production to detect performance drift, identify bugs, and trigger necessary updates.
Requirements
- 3+ years of experience in machine learning engineering or MLOps
- Proven experience deploying machine learning models to production environments and managing model lifecycle.
- Software engineering skills with experience in Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience setting up and managing infrastructure for machine learning models, including managing servers, cloud resources, and containers
- Experience automating machine learning workflows and pipelines (e.g., data preprocessing, model training, model deployment, monitoring).
- Solid understanding of statistical methods and principles
- Proficiency in Python and relevant data science libraries (e.g., scikit-learn, TensorFlow, PyTorch)
- Excellent communication and collaboration skills to bridge the gap between technical and business needs
- Stakeholder management skills to effectively identify, calculate, and communicate the value of data science initiatives
- Strong work ethic, self-motivation, and a passion for making a positive impact
Preferred Qualifications
- Experience working in a high growth, scaling environment
- Healthcare or software industry experience
- MS or Phd degree in Computer Science, Machine Learning, Data Mining, Statistics, or related technical field
This is a hybrid role that requires four days of in-person work.
Benefits
Charlie Health is pleased to offer comprehensive benefits to all full-time employees. Read more about our benefits here#LI-Hybrid
Additional Information
The total target base compensation for this role will be between $165,000 and $230,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.
