Senior Machine Learning Engineer
4 weeks ago
Charlie Health is a pioneering mental health treatment provider dedicated to connecting individuals with life-saving care. Our mission is to bridge the gap between those in need and accessible, high-quality treatment options. By leveraging cutting-edge technology and innovative approaches, we strive to make a tangible impact on the mental health landscape.
The RoleWe are seeking an experienced Machine Learning Engineer to join our tech team. As a key member of our data science efforts, you will play a pivotal role in operationalizing ML models that drive meaningful outcomes for our patients. Your expertise will be instrumental in scaling our data science infrastructure and ensuring seamless integration with production systems.
Responsibilities- Design and implement strategies for deploying and scaling machine learning models in a production environment.
- Collaborate with data scientists and engineers to deploy ML models into production systems, ensuring stability and performance.
- Develop and deploy APIs for machine learning models, ensuring seamless integration with production systems.
- Automate data pipelines, model training processes, and monitoring tasks to optimize operational efficiency and reduce manual intervention.
- Maintain and optimize the infrastructure that supports the deployment and execution of machine learning models, ensuring reliability and scalability.
- Continuously monitor models in production to detect performance drift, identify bugs, and trigger necessary updates.
- 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.
- 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.
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