Machine Learning Engineer III
Teladoc Health
- Argentina
- Permanente
- Tiempo completo
- Design, develop, deploy, and maintain production-grade scalable data transformation, machine learning, and deep learning code, pipelines, and operation dashboards; manage data and model versioning, training, tuning, serving, experiment, and evaluation tracking dashboards.
- Design and implement appropriate data warehouses and schemas for the ETL and machine learning pipelines. Manage ETL and machine learning model lifecycle: develop, deploy, monitor, maintain/debug, and update data and models in production.
- Use Python, SQL, Spark, Tensorflow, and PyTorch to write clean, reusable, and robust code for data engineering and machine learning pipelines. This includes data transformation, feature engineering, unsupervised learning, supervised learning, and reinforcement learning.
- Implement engineering solutions end to end including CI/CD, Scaling, Logging, Monitoring of Services, Alerting, Modeling work, Product integration, E2E testing, and defining SLAs between microservices.
- Promote and role-model best practices and framework of ML model development, testing, evaluation, operation and experimentation, etc. within the team and beyond.
- Self-driven individual with extensive experience in building and scaling maintainable software, data processing, feature extraction and construction and machine learning pipelines including model training, serialization, evaluation, interpretation and experimentation.
- 6+ years’ experience in Machine Learning Engineering roles in SaaS or consumer companies
- A Master’s degree or higher in computer science, machine learning, information systems, engineering, or a related field.
- Ability to write clean, robust and reusable code in Python, Spark, and SQL. Familiarity with big data platforms (like Spark), machine learning frameworks (like Tensorflow, Keras, or PyTorch), and libraries (like scikit-learn).
- Familiarities with the cloud platform, ETL, ML pipeline and API service tools like Azure, AWS, Jenkins, Databricks, sagemaker, MLflow, Flask, Airflow or similar.
- Deep knowledge of probability, statistics, and ML algorithms. Familiarity with deep learning, contextual bandits/reinforcement learning, and generative AI with experimentation experience in production would be a plus.
- Experience with agile sprint processes to deliver ML work.
- Willingness to learn new ML platforms and tools, as well as propose and help teams adopt new tools. Willingness to expand the scope to work with backend/frontend engineers, DevOps partners to solve the problem as needed.
- Great active listening skills to infer product needs and underlying context.
- Ability to collaborate effectively with peers, and respect for member privacy.