A Machine Learning Engineer is a professional who specializes in designing and implementing machine learning models and systems. They are responsible for developing algorithms that can learn and improve from data without explicit programming. 

Primary tasks and responsibilities of a machine learning engineer. 

     Data Preparation: Machine learning engineers work with large datasets, ensuring data quality by cleaning, preprocessing, and transforming the data. This involves tasks such as feature extraction, normalization, handling missing values, and addressing other data-related challenges. 

     Model Development: Machine learning engineers select and implement appropriate algorithms and techniques to build machine learning models based on project requirements. They fine-tune. model parameters, optimize performance, and address issues related to overfitting or underfitting. 

     Training and Evaluation: Machine learning engineers train models using prepared data and evaluate their performance. They employ various evaluation metrics to measure accuracy, precision, recall, and other relevant metrics. They also perform cross-validation or train-tet splits to assess how well the model is generalized. 

     Deployment and integration: Once trained and evaluated, machine learning engineers deploy models into production environments. They integrate the models with existing systems or develop new systems to leverage the models' prediction. They ensure scalability, efficiency, and reliability during the deployment process. 

     Performance Optimization: Machine learning engineers continuously optimize model performance. They fine-tune models, explore advanced algorithms, and employ techniques like feature engineering, dimensionality reduction, or ensemble methods to enhance performance. 

     Monitoring and Maintenance: After deployment, machine learning engineers monitor model performance in real-world scenarios. They track accuracy, troubleshoot, issues, and update models when new data becomes available or when performance deteriorates over time. 

     Collaboration: Machine learning engineers collaborate with cross-functional teams, including data scientists, software engineers, domain experts, and stakeholders. They work together to understand project requirements, communicate results, and ensure the successful implementation of machine-learning solutions. 

     Stay Updated: Machine learning engineers stay abreast of the latest advancements in the field. They continually learn new algorithms, techniques, frameworks, and tools to improve their skills and keep pace with the rapidly evolving field of machine learning.

To excel in a machine learning engineering career, it is beneficial to have a strong foundation in certain high school subjects.

Here are some subjects that can help prepare for this field.

     Mathematics: Mathematics forms the core foundation for machine learning. Focus on subjects like algebra, calculus, probability, and statistics, as they are crucial for understanding the underlying principles of machine learning algorithms. 

     Computer Science: Develop programming skills and gain knowledge of data structures and algorithms. Learn programming languages like Python and familiarize yourself with concepts such as object-orientated programming, algorithms, and data manipulation.

     Statistics: Knowledge of statistics helps in understanding probability distributions, hypothesis testing, and statistical modeling, which are fundamental to machine learning.  

     Information Technology: Familiarity with IT concepts, including computer systems, networks, and databases, can be beneficial for understanding the technical aspects of deploying machine learning models. 

While these subjects provide a good foundation, it is important to note that pursuing higher education in computer science, data science, or related fields would offer more comprehensive knowledge and practical experience in machine learning.