Memorial Sloan Kettering Cancer Center Machine Learning Engineer Interview Questions + Guide in 2025

Memorial Sloan Kettering Cancer Center (MSK) is a premier institution dedicated to the eradication of cancer through innovative research and personalized patient care.

As a Machine Learning Engineer at MSK, you will play a pivotal role in utilizing advanced computational techniques to enhance cancer diagnosis and treatment methodologies. Your primary responsibilities will include developing, training, and testing machine learning algorithms, specifically tailored for the analysis of large-scale digital pathology image data. This position requires a collaborative spirit, as you will work alongside a diverse team of machine learning experts, software engineers, and medical professionals, all united by the goal of improving patient outcomes through cutting-edge technology.

Key skills for success in this role include a strong foundation in algorithms and machine learning principles, proficiency in programming languages such as Python, and a solid understanding of statistical methodologies. Experience with high performance computing (HPC) and a background in medical imaging or computational pathology will set you apart as a candidate. Additionally, possessing a Doctorate in a related field, a robust publication history, and the ability to mentor others will significantly enhance your candidacy.

This guide will help you prepare effectively for the interview by emphasizing the skills and experiences that align with MSK's mission and values, ultimately giving you the confidence to showcase your fit for the role.

The interview process for a Machine Learning Engineer at Memorial Sloan Kettering Cancer Center is structured to assess both technical skills and cultural fit within the organization. It typically consists of three main rounds, each designed to evaluate different aspects of the candidate's qualifications and alignment with the center's mission.

1. Initial Phone Screening

The first step in the interview process is a phone screening, which usually lasts about 30 minutes. During this call, a recruiter will ask a series of background questions to understand your experience, skills, and motivations for applying to MSK. This is also an opportunity for you to express your interest in the organization and its mission in cancer research and treatment.

2. Technical Interview

Following the initial screening, candidates are invited to a technical interview, which may be conducted via video conferencing. This round typically lasts around 45 minutes and focuses on assessing your technical expertise in machine learning, deep learning, and programming languages such as Python. Expect to solve coding problems and discuss your previous projects, particularly those related to computational pathology or medical imaging. You may also be asked to explain complex algorithms and their applications in real-world scenarios.

3. Panel Interview

The final round is a panel interview, where you will meet with multiple team members, including engineers and possibly medical professionals. This round is more in-depth and may include a mix of technical and behavioral questions. You will be evaluated on your ability to collaborate within a diverse team, your problem-solving skills, and your understanding of the clinical implications of your work. Be prepared to discuss your research background, your approach to machine learning challenges, and how you can contribute to the ongoing projects at MSK.

Throughout the interview process, candidates are encouraged to ask questions about the team dynamics, ongoing research, and the impact of their work on cancer care.

Now, let's delve into the specific interview questions that candidates have encountered during this process.

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

Understand the Mission and Values

Memorial Sloan Kettering Cancer Center is deeply committed to ending cancer for life. Familiarize yourself with their mission, values, and recent advancements in cancer research and treatment. Be prepared to articulate how your skills and experiences align with their goals, particularly in the context of machine learning and its applications in healthcare. This will demonstrate your genuine interest in the organization and its mission.

Prepare for a Multi-Round Interview Process

The interview process typically consists of multiple rounds, including a phone screening, technical interviews, and meetings with team members. Expect a friendly yet thorough evaluation of your background and skills. Be ready to discuss your resume in detail, as interviewers will likely ask specific questions about your past experiences and projects. Practice articulating your career goals and how they align with the role at MSK.

Brush Up on Technical Skills

Given the emphasis on machine learning and deep learning, ensure you are well-versed in relevant algorithms and programming languages, particularly Python. Familiarize yourself with statistical methodologies and their applications in high-volume image data analysis. You may encounter coding challenges or technical questions, so practice solving problems on platforms like LeetCode or HackerRank, focusing on medium-level questions that reflect the complexity of the work at MSK.

Emphasize Collaboration and Communication

The role requires collaboration with a diverse team of machine learning experts, software engineers, and medical professionals. Highlight your ability to work in interdisciplinary teams and your experience in mentoring or guiding others. Be prepared to discuss specific examples of how you have successfully collaborated on projects, resolved conflicts, or communicated complex ideas to non-technical stakeholders.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and interpersonal skills. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on past experiences where you faced challenges, worked under pressure, or had to navigate difficult team dynamics. This will help you convey your resilience and ability to thrive in a fast-paced, research-driven environment.

Ask Insightful Questions

At the end of your interviews, take the opportunity to ask thoughtful questions about the team, ongoing projects, and the future direction of machine learning at MSK. Inquire about the specific challenges the team is currently facing and how your role could contribute to overcoming them. This not only shows your interest in the position but also your proactive approach to understanding the work environment.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This is a chance to reinforce your fit for the position and keep the lines of communication open. If you don’t hear back within the expected timeframe, don’t hesitate to follow up politely to inquire about your application status.

By preparing thoroughly and demonstrating your alignment with MSK's mission and values, you will position yourself as a strong candidate for the Machine Learning Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer at Memorial Sloan Kettering Cancer Center. The interview process will likely focus on your technical expertise in machine learning, deep learning, and computer vision, as well as your ability to work collaboratively in a research-driven environment. Be prepared to discuss your past experiences, problem-solving skills, and how you align with the mission of the organization.

Machine Learning and Deep Learning

1. Can you explain the differences between supervised, unsupervised, and semi-supervised learning?

Understanding these concepts is fundamental in machine learning, especially in a clinical context where data can be scarce or unlabelled.

How to Answer

Discuss the definitions of each learning type, providing examples of when each might be used, particularly in medical imaging or pathology.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as classifying images of cancerous versus non-cancerous cells. Unsupervised learning, on the other hand, deals with unlabeled data, allowing the model to identify patterns or groupings, which can be useful in exploratory data analysis. Semi-supervised learning combines both approaches, leveraging a small amount of labeled data with a larger set of unlabeled data, which is often the case in medical datasets.”

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

This question assesses your practical experience and problem-solving abilities in real-world applications.

How to Answer

Detail the project scope, your role, the challenges encountered, and how you overcame them, emphasizing any relevant experience in healthcare or pathology.

Example

“I worked on a project aimed at predicting patient outcomes based on imaging data. One major challenge was dealing with imbalanced classes, as there were significantly fewer positive cases. I implemented techniques such as SMOTE for oversampling and adjusted the class weights in the loss function, which improved our model's performance significantly.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics, which are crucial in clinical applications.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain their relevance in a medical context.

Example

“I evaluate model performance using a combination of metrics. For instance, in a cancer detection model, precision and recall are critical; high precision ensures that we minimize false positives, while high recall ensures that we catch as many true cases as possible. I also use ROC-AUC to assess the trade-off between sensitivity and specificity.”

4. What techniques do you use to prevent overfitting in your models?

Overfitting is a common issue in machine learning, especially with complex models like deep learning.

How to Answer

Mention techniques such as cross-validation, regularization, dropout, and early stopping, and provide context on their application.

Example

“To prevent overfitting, I employ techniques like cross-validation to ensure that my model generalizes well to unseen data. I also use regularization methods such as L1 and L2 to penalize overly complex models. Additionally, I implement dropout layers in neural networks to randomly deactivate neurons during training, which helps in reducing overfitting.”

Statistics and Probability

1. How would you explain the concept of p-value to a non-technical audience?

This question assesses your ability to communicate complex statistical concepts clearly.

How to Answer

Simplify the concept of p-value and relate it to practical scenarios, especially in a healthcare context.

Example

“A p-value helps us understand the strength of our evidence against a null hypothesis. In simpler terms, if we have a p-value of 0.05, it means there’s a 5% chance that the observed results could happen by random chance. In healthcare, this helps us determine whether a treatment is effective or if the results we see are just due to chance.”

2. Can you discuss the importance of statistical significance in clinical trials?

This question evaluates your understanding of statistics in the context of medical research.

How to Answer

Explain the role of statistical significance in determining the efficacy of treatments and how it impacts patient care.

Example

“Statistical significance is crucial in clinical trials as it helps us determine whether the observed effects of a treatment are likely due to the treatment itself rather than random variation. This is vital for making informed decisions about patient care and ensuring that new treatments are both effective and safe.”

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

This question tests your foundational knowledge of statistics, which is essential for data analysis.

How to Answer

Define the Central Limit Theorem and explain its implications in the context of sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means will approach a normal distribution as the sample size increases, regardless of the original distribution of the data. This is important because it allows us to make inferences about population parameters even when the underlying data is not normally distributed, which is often the case in medical research.”

4. How do you handle missing data in your datasets?

Handling missing data is a critical skill in data science, especially in healthcare datasets.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If the missing data is minimal, I might use imputation techniques like mean or median substitution. For larger gaps, I consider using algorithms that can handle missing values directly or employing multiple imputation methods to create several complete datasets and average the results.”

Algorithms and Data Structures

1. Can you explain the difference between a stack and a queue?

This question tests your understanding of fundamental data structures.

How to Answer

Define both data structures and provide examples of their use cases.

Example

“A stack is a Last In First Out (LIFO) structure, where the last element added is the first to be removed, like a stack of plates. A queue, on the other hand, is a First In First Out (FIFO) structure, where the first element added is the first to be removed, similar to a line of people waiting for service. In machine learning, stacks can be used for backtracking algorithms, while queues are often used in breadth-first search algorithms.”

2. Describe a situation where you had to optimize an algorithm. What approach did you take?

This question assesses your problem-solving skills and understanding of algorithm efficiency.

How to Answer

Discuss a specific example where you identified inefficiencies and the steps you took to optimize the algorithm.

Example

“I worked on an image processing algorithm that was taking too long to execute. I analyzed the time complexity and identified that nested loops were causing inefficiencies. I optimized it by using vectorized operations with NumPy, which reduced the execution time significantly and improved the overall performance of the application.”

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

This question tests your knowledge of algorithm efficiency.

How to Answer

Explain the concept of time complexity and provide the time complexity of binary search.

Example

“The time complexity of a binary search algorithm is O(log n), where n is the number of elements in the sorted array. This efficiency comes from the fact that the algorithm divides the search interval in half with each iteration, allowing it to quickly narrow down the potential location of the target value.”

4. How would you implement a function to reverse a string?

This question assesses your coding skills and understanding of basic algorithms.

How to Answer

Describe the approach you would take to reverse a string, mentioning any specific programming languages or techniques you would use.

Example

“To reverse a string, I would use Python’s slicing feature, which allows for a concise implementation. The function would look like this: reversed_string = original_string[::-1]. This approach is efficient and leverages Python’s built-in capabilities to handle string manipulation.”