Machine Learning Engineer at DentalMonitoring developing and improving ML models for orthodontic treatment solutions. Engage in deep learning and computer vision tasks within a hybrid work environment.
About the role
- Software Engineer developing and scaling machine learning systems for a consumer finance platform. Collaborating with data engineers and deploying models in cloud environments.
Responsibilities
- Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
- Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
- Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
- Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation.
- Apply machine learning design patterns to build modular, reusable, and production-ready models.
- Collaborate with data engineers to develop high-performance data pipelines for training and inference.
- Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
- Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
Requirements
- Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows.
- Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
- Knowledge of cloud-based ML deployment and infrastructure management.
- Ability to implement real-time and batch inference pipelines efficiently.
- Strong analytical and problem-solving skills to translate business needs into scalable ML solutions.
- Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy.
Benefits
- $250 monthly lunch stipend
- $150 monthly commuter stipend
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
AI/ML Engineer developing advanced solutions for federal client at Niyam IT. Focusing on AI/ML models and operational efficiency improvement in federal environments.
Senior Machine Learning Engineer at Capital Group, designing Generative AI systems to enhance investment processes. Collaborating with professionals to translate ideas into reliable, production - grade systems
Senior AI/ML Engineer designing and deploying AI/ML solutions for validation challenges at Micron. Working on machine learning models and optimization solutions to enhance product quality and validation efficiency.
Machine Learning Engineer focusing on MLOps and software engineering at flaschenpost, ensuring robust planning and operational success through ML products.
AI ML Engineer at global networking leader, shaping ML strategy and building high - performance systems. Innovating with AI technology to enhance network management and develop flagship products.
Senior Staff Machine Learning Engineer leading technical architecture for GEICO's AI Agent Platform. Driving innovation and enhancing productivity for internal associates and customers.
Staff Machine Learning Engineer developing the next generation of AI Agent OS and SDKs for GEICO. Key responsibilities include architecting scalable systems and implementing observability frameworks.
Senior Machine Learning Engineer at Bumble developing scalable AI systems for personalized user interactions. Leading machine learning model development and deployment from exploration to production.
Lead Machine Learning Engineer at Bumble shaping user connections through machine learning. Driving end - to - end AI solutions while mentoring engineers in a hybrid work environment.