Machine Learning Engineer

Primary Activities

A Machine Learning Engineer in the Technology function is typically expected to perform the following activities as a part of their job. Expect questions delving deeper into these areas depending on your level of experience. This is a representative list and not a complete one; the latter are generally based on the exact nature of the role

Developing Machine Learning Models: Designing and implementing machine learning algorithms and models to solve complex problems
Data Preprocessing & Feature Engineering: Cleaning, transforming, and preparing data for analysis, including feature selection and extraction
Training & Evaluating Models: Training machine learning models using available data and evaluating their performance using appropriate metrics
Optimizing & Tuning Models: Fine-tuning machine learning models to improve their accuracy, efficiency, and generalization capabilities
Deploying Models Into Production: Integrating machine learning models into production systems, ensuring scalability, reliability, and performance
Monitoring & Maintaining Models: Continuously monitoring the performance of deployed models, identifying and resolving issues, and updating models as needed
Collaborating With Cross Functional Teams: Working closely with data scientists, software engineers, and other stakeholders to understand requirements and deliver ML solutions
Staying Up To Date With Ml Advancements: Keeping abreast of the latest research, techniques, and tools in machine learning to enhance skills and knowledge
Documenting & Communicating Results: Preparing clear and concise documentation of ML models, methodologies, and findings, and effectively communicating them to technical and non-technical audiences

Key Performance Indicators

Machine Learning Engineers in the Technology function are often evaluated using the following KPI metrics. Address atleast some of these metrics in your resume line items & within your interview stories to maximize your prospects (if you have prior experiences in this or a related role). This is not a comprehensive list and exact metrics vary depending on the type of business

Model Accuracy: Measures the accuracy of machine learning models in predicting outcomes
Precision: Evaluates the proportion of true positive predictions out of all positive predictions made by the model
Recall: Measures the proportion of true positive predictions out of all actual positive instances in the dataset
F1 Score: Combines precision and recall into a single metric to assess the overall performance of a model
Mean Absolute Error (MAE): Calculates the average absolute difference between predicted and actual values
Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values
Root Mean Squared Error (RMSE): The square root of MSE, providing a more interpretable error metric
Mean Absolute Percentage Error (MAPE): Calculates the average percentage difference between predicted and actual values
R-squared: Indicates the proportion of the variance in the dependent variable that can be explained by the independent variables
Training Time: Measures the time taken to train a machine learning model
Inference Time: Evaluates the time taken to make predictions using a trained model
Data Preprocessing Time: Measures the time taken to preprocess and clean the input data before training the model
Feature Importance: Determines the importance of each feature in contributing to the model's predictions
Model Size: Measures the size of the machine learning model in terms of memory or disk space
Training Convergence: Evaluates whether the training process has converged and the model has reached optimal performance
Bias-Variance Tradeoff: Balances the model's ability to fit the training data (low bias) with its ability to generalize to unseen data (low variance)

Selection Process

Successful candidates for a Machine Learning Engineers role in the Technology function can expect a similar selection process as the one outlined below. Actual process may vary depending on seniority, size/type of company etc.

Phone screening
Initial phone call to assess qualifications and fit
Technical interview
In-depth technical discussion and assessment of machine learning knowledge and skills
Coding challenge
Practical coding exercise to evaluate programming abilities
Machine learning project review
Review of past machine learning projects and their outcomes
Behavioral interview
Assessment of soft skills, teamwork, and problem-solving approach
On-site interview
In-person interviews with various team members and stakeholders
Presentation
Presenting a machine learning project or solution to a panel
Reference check
Contacting provided references to gather insights on candidate's work ethic and performance
Final decision
Evaluation of all interview feedback to make a hiring decision

Interview Questions

Common Interview Questions that a Machine Learning Engineers in the Technology function is likely to face. Prepare stories that tailor to your own experiences that may help you answer these questions effectively. This is not a complete list and more questions will be added over time. Use the topic tags in the search box below to filter by specific topics

Link	Question	Topic(s)
Link	What is the difference between supervised and unsupervised learning?	Machine Learning Concepts
Link	Explain the bias-variance tradeoff.	Machine Learning Concepts
Link	What is regularization in machine learning?	Machine Learning Concepts
Link	What is the purpose of cross-validation?	Machine Learning Concepts
Link	What is the difference between bagging and boosting?	Machine Learning Concepts
Link	What evaluation metrics would you use for a classification problem?	Machine Learning Concepts
Link	Explain the concept of overfitting in machine learning.	Machine Learning Concepts
Link	What is the difference between precision and recall?	Machine Learning Concepts
Link	How would you handle imbalanced datasets in machine learning?	Machine Learning Concepts
Link	What is the purpose of feature scaling in machine learning?	Machine Learning Concepts
Link	What is the difference between a generative and discriminative model?	Machine Learning Concepts
Link	Explain the concept of gradient descent.	Machine Learning Algorithms
Link	What is the difference between batch gradient descent and stochastic gradient descent?	Machine Learning Algorithms
Link	What is the purpose of the activation function in a neural network?	Machine Learning Algorithms
Link	Explain the concept of backpropagation.	Machine Learning Algorithms
Link	What is the difference between L1 and L2 regularization?	Machine Learning Algorithms
Link	How would you handle missing data in a dataset?	Data Preprocessing
Link	What is dimensionality reduction and why is it important?	Data Preprocessing
Link	What is the purpose of feature extraction in machine learning?	Data Preprocessing
Link	Explain the concept of one-hot encoding.	Data Preprocessing