84.51 Degrees Machine Learning Engineer Interview Questions + Guide in 2025

84.51 Degrees is a retail data science, insights, and media company dedicated to enhancing the shopping experience by leveraging extensive consumer data.

The Machine Learning Engineer role at 84.51 Degrees involves a unique blend of computer science and data science to develop computationally efficient software solutions. Key responsibilities include providing technical leadership in machine learning tool development, researching innovative algorithms, and building scalable production solutions. A strong grasp of algorithms, Python, and distributed data processing technologies like Spark is essential, coupled with the ability to communicate technical concepts clearly across diverse teams. Ideal candidates will demonstrate independence, strong project management skills, and a passion for learning and implementing new technologies. This guide will help you prepare for an interview by highlighting the essential skills and knowledge areas that 84.51 Degrees values in a Machine Learning Engineer.

The interview process for a Machine Learning Engineer at 84.51° is designed to assess both technical skills and cultural fit within the company. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Screening

The process begins with an initial screening, which is usually conducted via a phone call with a recruiter. This conversation lasts about 30 minutes and serves to gauge your interest in the role, your background, and your fit for the company culture. Expect to discuss your resume, relevant experiences, and motivations for applying to 84.51°.

2. Technical Interview

Following the initial screening, candidates typically participate in a technical interview. This may be conducted virtually or in-person and often involves two engineers. During this stage, you will be asked to solve coding problems, which may include questions related to algorithms, data structures, and specific programming languages such as Python and SQL. You might also be presented with case study questions that require you to demonstrate your problem-solving skills and understanding of machine learning concepts.

3. Behavioral Interview

The behavioral interview is another critical component of the process. This round focuses on your past experiences and how they relate to the role. Expect to answer questions using the STAR (Situation, Task, Action, Result) method, discussing your teamwork, leadership, and conflict resolution skills. Interviewers will be interested in understanding how you approach challenges and collaborate with others.

4. Onsite or Final Round Interview

The final round often includes multiple interviews with various team members, including technical leads and management. This stage may involve a mix of technical assessments, case studies, and discussions about your previous projects. You may be asked to explain complex concepts in simple terms, demonstrating your ability to communicate effectively with both technical and non-technical stakeholders.

5. Offer and Feedback

After the final interviews, candidates typically receive feedback within a few days. If selected, you will be presented with an offer, which may include discussions about salary, benefits, and other employment terms.

As you prepare for your interview, it's essential to familiarize yourself with the types of questions that may be asked during each stage.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at 84.51° typically begins with a screening call, followed by a series of interviews that may include behavioral and technical assessments. Familiarize yourself with this structure so you can prepare accordingly. Expect to engage in discussions about your resume, past projects, and your interest in the company. Being prepared for both technical questions and personal anecdotes will help you navigate the conversation smoothly.

Prepare for Technical Questions

Given the emphasis on algorithms and Python in the role, brush up on your knowledge of machine learning concepts, particularly those related to algorithms and their applications. Be ready to discuss your experience with frameworks like TensorFlow or PyTorch, and be prepared to solve coding problems on the spot. Practice explaining complex concepts in simple terms, as you may be asked to clarify your thought process to non-technical stakeholders.

Showcase Your Problem-Solving Skills

During the interview, you may encounter case study questions or hypothetical scenarios that require you to demonstrate your analytical thinking. Practice articulating your thought process clearly and logically. For example, you might be asked how you would approach a problem like optimizing a machine learning model or designing a data pipeline. Use the STAR method (Situation, Task, Action, Result) to structure your responses effectively.

Emphasize Collaboration and Communication

84.51° values teamwork and communication, so be prepared to discuss how you have collaborated with others in past projects. Highlight your ability to work with cross-functional teams and your experience in educating stakeholders about technical topics. Demonstrating your interpersonal skills will show that you can thrive in their collaborative environment.

Stay Current with Industry Trends

The company is focused on leveraging cutting-edge technology and staying ahead of industry trends. Show your enthusiasm for continuous learning by discussing recent advancements in machine learning or data science that excite you. This not only demonstrates your passion for the field but also your commitment to bringing innovative solutions to the company.

Be Yourself

Interviews at 84.51° are described as friendly and conversational. While it's important to showcase your skills and experience, don't forget to let your personality shine through. Be genuine in your responses and express your enthusiasm for the role and the company. This will help you connect with your interviewers and leave a lasting impression.

By following these tips, you can approach your interview with confidence and a clear strategy, setting yourself up for success in securing a position as a Machine Learning Engineer at 84.51°. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at 84.51 degrees. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of machine learning concepts, as well as your ability to communicate complex ideas clearly.

Machine Learning

1. Can you explain the difference between linear regression and logistic regression?

Understanding the distinction between these two fundamental algorithms is crucial for any machine learning engineer.

How to Answer

Clearly define both types of regression, emphasizing their use cases and the nature of the output they produce.

Example

"Linear regression is used for predicting continuous outcomes, while logistic regression is used for binary classification problems. In linear regression, the output is a real number, whereas logistic regression outputs probabilities that can be mapped to two classes using a threshold."

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in real-world scenarios.

How to Answer

Discuss the project scope, your role, the challenges encountered, and how you overcame them.

Example

"I worked on a recommendation system for an e-commerce platform. One challenge was dealing with sparse data. I implemented collaborative filtering techniques and used matrix factorization to improve recommendations, which significantly enhanced user engagement."

3. How would you approach feature selection for a machine learning model?

Feature selection is critical for model performance, and interviewers want to know your methodology.

How to Answer

Explain your process for selecting features, including any techniques or tools you use.

Example

"I typically start with domain knowledge to identify potential features, followed by techniques like correlation analysis and recursive feature elimination. I also use model-based methods, such as feature importance from tree-based models, to refine my selection."

4. What is overfitting, and how can you prevent it?

Understanding overfitting is essential for building robust models.

How to Answer

Define overfitting and discuss strategies to mitigate it.

Example

"Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation, regularization, and pruning in decision trees."

5. Can you explain the concept of embeddings and their use in machine learning?

Embeddings are a key concept in modern machine learning, especially in natural language processing.

How to Answer

Discuss what embeddings are and how they are applied in various contexts.

Example

"Embeddings are dense vector representations of data, often used to capture semantic relationships. For instance, in NLP, word embeddings like Word2Vec or GloVe allow models to understand context and similarity between words."

Algorithms

1. Describe a time when you had to optimize an algorithm for performance. What steps did you take?

This question evaluates your problem-solving and optimization skills.

How to Answer

Detail the algorithm, the performance issues, and the optimization techniques you applied.

Example

"I was tasked with optimizing a sorting algorithm that was running too slowly on large datasets. I analyzed its time complexity and switched from a bubble sort to a quicksort algorithm, which improved performance significantly."

2. What is the time complexity of a binary search algorithm?

Understanding algorithm efficiency is crucial for a machine learning engineer.

How to Answer

Provide the time complexity and explain the reasoning behind it.

Example

"The time complexity of a binary search algorithm is O(log n) because it divides the search space in half with each iteration, making it much more efficient than a linear search."

3. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data preprocessing.

How to Answer

Discuss various strategies for dealing with missing data.

Example

"I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, such as mean or median substitution, or I may choose to remove records with missing values if they are not significant."

4. Explain the concept of a confusion matrix and its importance.

A confusion matrix is a vital tool for evaluating classification models.

How to Answer

Define a confusion matrix and explain its components and significance.

Example

"A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives. It helps in calculating metrics like accuracy, precision, recall, and F1-score, which are crucial for model evaluation."

5. What are some common algorithms used for clustering?

Clustering is a fundamental unsupervised learning technique.

How to Answer

List common clustering algorithms and briefly describe their use cases.

Example

"Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN. K-means is often used for partitioning data into K distinct clusters, while DBSCAN is effective for identifying clusters of varying shapes and densities."

Statistics & Probability

1. What is the Central Limit Theorem, and why is it important?

The Central Limit Theorem is a foundational concept in statistics.

How to Answer

Explain the theorem and its implications for statistical analysis.

Example

"The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is important because it allows us to make inferences about population parameters using sample statistics."

2. How do you interpret p-values in hypothesis testing?

Understanding p-values is crucial for statistical analysis.

How to Answer

Define p-values and explain their significance in hypothesis testing.

Example

"A p-value indicates the probability of observing the data, or something more extreme, given that the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that the observed effect is statistically significant."

3. Can you explain the difference between Type I and Type II errors?

This question tests your understanding of hypothesis testing errors.

How to Answer

Define both types of errors and provide examples.

Example

"A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For example, a Type I error could mean concluding a drug is effective when it is not, while a Type II error could mean failing to detect an effect that is present."

4. What is the purpose of A/B testing?

A/B testing is a common method for evaluating changes in a system.

How to Answer

Explain the concept and its application in decision-making.

Example

"A/B testing involves comparing two versions of a variable to determine which one performs better. It's widely used in marketing and product development to make data-driven decisions based on user behavior."

5. How would you explain the concept of statistical significance to a non-technical audience?

This question assesses your ability to communicate complex ideas simply.

How to Answer

Use analogies or simple language to convey the concept.

Example

"I would explain statistical significance as a way to determine if the results we see are likely due to chance or if they reflect a real effect. It's like flipping a coin: if you get heads 10 times in a row, it's unlikely to be random, suggesting something else is at play."