Top 20 US Jobs ML Engineer jobs in USA with Recommendations Engine Experience || Plano, TX (Onsite) || TechM Quick Apply

ML Engineer

The role of a Machine Learning (ML) Engineer involves developing, deploying, and maintaining machine learning models to solve real-world problems. Here are 20 common job responsibilities for a Machine Learning Engineer:

1. Problem Definition: Work with stakeholders to define and understand business problems that can be addressed with machine learning solutions.
2. Data Collection: Collect and gather relevant data from various sources for training machine learning models.
3. Data Cleaning and Preprocessing: Clean and preprocess raw data to ensure its quality and prepare it for model training.
4. Feature Engineering: Identify and engineer features that are relevant to the problem at hand to improve model performance.
5. Model Development: Develop and implement machine learning models using algorithms suitable for the specific problem.
6. Model Training: Train machine learning models on labeled datasets, optimizing for accuracy, precision, recall, or other relevant metrics.
7. Hyperparameter Tuning: Fine-tune model hyperparameters to improve performance and generalization.
8. Model Evaluation: Evaluate model performance using appropriate metrics and iterate on model improvements.
9. Deployment: Deploy machine learning models to production environments, considering scalability, efficiency, and reliability.
10. Monitoring and Maintenance: Implement monitoring systems to track model performance in real-world scenarios and maintain models over time.
11. Version Control: Use version control systems to manage and track changes in the codebase and model versions.

1. Big Data Technologies: Work with big data technologies such as Apache Spark or others for processing large datasets.
2. Cloud Platforms: Deploy and manage machine learning models on cloud platforms like AWS, Azure, or Google Cloud.
3. API Integration: Integrate machine learning models with other software systems and APIs.
4. Collaboration with Data Scientists: Collaborate with data scientists to understand model requirements and incorporate insights from data analysis into model development.
5. Continuous Learning: Stay updated on the latest developments in machine learning, deep learning, and relevant technologies.
6. Optimization: Optimize models for performance and efficiency, especially in resource-constrained environments.
7. Security Considerations: Implement security best practices in model deployment to protect against potential vulnerabilities.
8. Documentation: Create documentation for models, code, and processes to facilitate collaboration and knowledge transfer.
9. Cross-Functional Collaboration: Collaborate with other teams, such as software engineering, product management, and business analysts, to integrate machine learning solutions into overall business processes.

ML Engineers often work at the intersection of software engineering and data science, requiring a strong foundation in both areas. Effective communication and collaboration skills are also crucial for successful ML engineering, as it involves working with diverse teams and stakeholders.