Middle/Senior Machine Learning Engineer

As a Middle/Senior Machine Learning Engineer at our company, your main responsibilities include developing and maintaining robust data pipelines for training ML models, automating workflows to streamline deployment processes, collaborating with data scientists, and monitoring model performance. You'll play a critical role in ensuring data quality within our Amazon Feature Store, contributing to new feature development while continuously optimizing existing models.

To qualify for the Middle/Senior Machine Learning Engineer role, you should have proven experience with ML frameworks and algorithms such as CatBoost, XGBoost, and Neural Networks. A strong background in building and maintaining ML pipelines, along with proficiency in Python and familiarity with libraries like Scikit-learn, TensorFlow, and PyTorch is essential. Additionally, a solid understanding of data engineering concepts and good spoken English skills are required.

In this role, a Middle/Senior Machine Learning Engineer will work on diverse projects that involve building and optimizing data pipelines, deploying features onto our Amazon Feature Store, and enhancing model performance. You'll collaborate closely with data scientists to create scalable machine learning solutions tailored to specific business needs, while continuously adapting to new challenges.

As a Middle/Senior Machine Learning Engineer, you will frequently work with cloud platforms like AWS, utilizing tools such as the Amazon Feature Store. Your projects will involve ML frameworks and libraries including CatBoost, XGBoost, TensorFlow, and PyTorch, along with your programming expertise in Python to build seamless data pipelines and deploy machine learning models.

Our company strongly emphasizes professional development and growth opportunities for Middle/Senior Machine Learning Engineers. This includes annual performance reviews, support for attendance at industry-related events, and access to resources for purchasing necessary software and technology. We're committed to fostering an environment where you can enhance your skills and advance your career.

When discussing a previous machine learning project, focus on your role, the challenges you faced, and how you solved them. Explain the algorithms you used, how you processed the data, and any collaboration you had with team members. Highlight the impact your work had on the project outcomes.

To ensure model accuracy, begin by selecting the right metrics and evaluation techniques based on your problem statement. Discuss the importance of keeping the data set fresh, using cross-validation, and adjusting hyperparameters. Emphasize the process of retraining the model with new data and monitoring performance over time.

Express your personal preferences among frameworks like TensorFlow, PyTorch, or Scikit-learn. Share specific use cases where you found these frameworks particularly beneficial, whether due to ease of use, community support, or robust functionality for certain algorithms.

Talk about your exposure to data engineering, including data preparation, pipeline development, and ETL processes. Highlight any tools you've worked with, such as Apache Airflow or Spark, that have helped automate and optimize data workflows for ML model training.

Discuss your systematic approach to feature engineering, which may involve identifying relevant features, transforming raw data into usable formats, and leveraging domain knowledge. Provide examples of how you've created impactful features in past projects, and how this improved model performance.

Explain that hyperparameter tuning is critical for optimizing model performance and preventing overfitting or underfitting. Discuss various techniques like grid search or random search, and reiterate the importance of using validation sets to assess the model's performance during tuning.

Describe a methodical approach, starting with analyzing data quality issues, ensuring relevant features are included, and checking for overfitting or underfitting. Also, mention adjusting hyperparameters or trying different algorithms to find a better fit for the data.

Share the methods you use to stay updated, such as following influential publications, attending conferences, participating in discussions on forums like Stack Overflow, or taking relevant online courses. Demonstrate your passion for continuous learning in the field.

Illustrate your experience working with non-technical stakeholders by describing how you communicated complex ML concepts in a simple way. Detail how you gathered requirements and provided updates, ensuring all parties understood the project's objectives and progress.

Discuss the key metrics you rely on based on the type of project, such as accuracy, precision, recall, F1-score, or AUC for classification tasks, and RMSE or MAE for regression tasks. Explain how these metrics help you make informed decisions about model selection and improvements.