Tempus Labs Machine Learning Engineer Interview Questions + Guide in 2025

Tempus Labs is at the forefront of precision medicine, leveraging AI and data analytics to enhance clinical care and provide actionable insights for healthcare providers.

As a Machine Learning Engineer at Tempus, you will be responsible for developing and implementing advanced machine learning models that analyze complex biological datasets, particularly in the realm of cancer precision medicine. Key responsibilities include researching and creating state-of-the-art algorithms for spatial transcriptomics analytics, collaborating with cross-functional teams to integrate insights into the Tempus ecosystem, and deploying robust machine learning models for both structured and unstructured data. A successful candidate will possess a PhD in a quantitative field, significant experience with genomic data, and a deep understanding of machine learning methodologies, particularly in handling high-dimensional datasets. Your ability to communicate complex findings clearly and work in a collaborative environment will be essential to contributing to Tempus's mission of advancing healthcare through technology.

This guide will help you navigate the interview process by providing insights into the skills and experiences that are crucial for success in this role, allowing you to present yourself as a strong candidate who aligns with Tempus's goals and values.

The interview process for a Machine Learning Engineer at Tempus Labs is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role and the company culture. The process typically unfolds in several key stages:

1. Initial Recruiter Screen

The first step involves a 30-minute phone interview with a recruiter. This conversation focuses on your background, interest in the role, and basic qualifications. The recruiter will also provide insights into Tempus Labs' work culture and the specifics of the Machine Learning Engineer position. This is an opportunity for you to ask questions about the company and the role to gauge if it aligns with your career goals.

2. Technical Assessment

Following the initial screen, candidates are usually required to complete a take-home coding challenge. This assessment typically includes tasks related to algorithms and data manipulation, often requiring proficiency in Python and familiarity with machine learning concepts. The challenge is designed to evaluate your coding skills, problem-solving abilities, and understanding of machine learning principles.

3. Technical Interviews

Candidates who successfully complete the take-home assignment will move on to a series of technical interviews. These interviews may be conducted via video conferencing and typically consist of multiple rounds. Expect to engage in discussions that cover system design, coding exercises (often based on LeetCode-style questions), and deep dives into your previous projects and experiences. Interviewers will assess your ability to apply machine learning algorithms to real-world problems, particularly in the context of genomic and multimodal data.

4. Behavioral Interviews

In addition to technical assessments, candidates will participate in behavioral interviews. These sessions often involve a panel of interviewers, including team members and managers. Questions will focus on your past experiences, teamwork, and how you handle challenges. Be prepared to discuss scenarios that demonstrate your problem-solving skills, adaptability, and ability to communicate complex ideas effectively.

5. Final Interview Round

The final stage typically includes a conversation with higher-level management or directors. This round may involve discussing your take-home assignment in detail, as well as your overall fit within the team and the organization. Expect to articulate your vision for the role and how you can contribute to Tempus Labs' mission in precision medicine.

Throughout the process, communication is key. Candidates have noted varying levels of responsiveness from the recruitment team, so it’s advisable to follow up if you haven’t heard back within the expected timeframe.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

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

Understand the Interview Process

Tempus Labs has a structured interview process that typically includes a recruiter screen, a take-home coding challenge, and multiple rounds of interviews with team members. Familiarize yourself with this process and prepare accordingly. Be ready to discuss your take-home assignment in detail, as it will likely be a focal point during your interviews.

Master the Technical Skills

Given the emphasis on algorithms and machine learning in this role, ensure you are well-versed in relevant technical skills. Brush up on your knowledge of algorithms, particularly those related to machine learning and data analysis. Practice coding problems on platforms like LeetCode, focusing on medium-level questions that require a solid understanding of data structures and algorithms. Additionally, be prepared to discuss your experience with Python and any machine learning frameworks you have used, such as TensorFlow or PyTorch.

Prepare for Behavioral Questions

Tempus values cultural fit, so expect behavioral questions that assess your teamwork, problem-solving abilities, and adaptability. Reflect on your past experiences and prepare to share specific examples that demonstrate your strengths and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Communicate Clearly and Confidently

Strong communication skills are essential for this role, especially when presenting complex findings to diverse stakeholders. Practice explaining your technical work in simple terms, as you may need to communicate with individuals who do not have a technical background. Be prepared to discuss how you would document and present your findings to your peers and other teams.

Show Enthusiasm for the Company’s Mission

Tempus is focused on advancing precision medicine and improving patient outcomes. Demonstrate your passion for this mission during the interview. Research the company’s recent projects and initiatives, and be ready to discuss how your skills and experiences align with their goals. This will show that you are not only a qualified candidate but also genuinely interested in contributing to their mission.

Be Ready for a Collaborative Environment

Collaboration is key at Tempus, as you will be working closely with cross-functional teams. Be prepared to discuss your experience working in collaborative settings and how you approach teamwork. Highlight any instances where you successfully integrated your work with others or contributed to a team project.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview and reiterate your interest in the role. This not only shows professionalism but also keeps you on the interviewers' radar.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Tempus Labs. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Tempus Labs. The interview process will likely focus on your technical skills in machine learning, algorithms, and programming, as well as your ability to communicate complex ideas effectively. Be prepared to discuss your experience with genomic data, spatial transcriptomics, and your approach to problem-solving in a collaborative environment.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each.

How to Answer

Discuss the key differences, such as the presence of labeled data in supervised learning and the absence of labels in unsupervised learning. Provide examples of algorithms used in each category.

Example

“Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. For instance, in a classification task, we might use algorithms like logistic regression or support vector machines. In contrast, unsupervised learning deals with unlabeled data, where the model tries to identify patterns or groupings, such as with clustering algorithms like K-means.”

2. How would you handle class imbalance in a dataset?

This question assesses your understanding of practical challenges in machine learning.

How to Answer

Discuss techniques such as resampling methods, using different evaluation metrics, or employing algorithms that are robust to class imbalance.

Example

“To address class imbalance, I would first analyze the distribution of classes and consider techniques like oversampling the minority class or undersampling the majority class. Additionally, I might use evaluation metrics like F1-score or AUC-ROC instead of accuracy to better reflect the model's performance on imbalanced data.”

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

This question allows you to showcase your practical experience.

How to Answer

Outline the project scope, your role, the methodologies used, and the challenges encountered, along with how you overcame them.

Example

“In a recent project, I developed a predictive model for patient outcomes using electronic health records. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. The model ultimately improved prediction accuracy by 15% compared to the baseline.”

4. What are some common metrics used to evaluate machine learning models?

This question tests your knowledge of model evaluation.

How to Answer

Mention various metrics and explain when to use each.

Example

“Common metrics include accuracy, precision, recall, F1-score, and AUC-ROC. For instance, while accuracy is useful for balanced datasets, precision and recall are more informative for imbalanced datasets, especially in medical applications where false negatives can be critical.”

Algorithms

1. Can you explain how a decision tree works?

This question assesses your understanding of fundamental algorithms.

How to Answer

Describe the structure of a decision tree and how it makes decisions based on feature values.

Example

“A decision tree splits the data into subsets based on the value of input features, creating branches that lead to decision nodes or leaf nodes. The splits are determined by criteria like Gini impurity or information gain, allowing the model to make predictions based on the majority class in the leaf nodes.”

2. What is overfitting, and how can it be prevented?

This question evaluates your understanding of model performance.

How to Answer

Define overfitting and discuss techniques to mitigate it.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization on unseen data. To prevent overfitting, I use techniques such as cross-validation, pruning in decision trees, and regularization methods like L1 and L2.”

3. Describe the concept of cross-validation. Why is it important?

This question tests your knowledge of model validation techniques.

How to Answer

Explain what cross-validation is and its purpose in model evaluation.

Example

“Cross-validation is a technique used to assess how a model will generalize to an independent dataset. It involves partitioning the data into subsets, training the model on some subsets while validating it on others. This process helps to ensure that the model is not overfitting and provides a more reliable estimate of its performance.”

4. What is the purpose of feature engineering in machine learning?

This question assesses your understanding of data preprocessing.

How to Answer

Discuss the importance of feature engineering and its impact on model performance.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. It is crucial because the right features can significantly enhance the model's ability to learn patterns. For example, in a healthcare dataset, creating features that capture interactions between different variables can lead to better predictive accuracy.”

Programming and Technical Skills

1. What programming languages are you proficient in, and how have you used them in your projects?

This question evaluates your technical skills.

How to Answer

Mention the languages you are proficient in and provide examples of how you have applied them.

Example

“I am proficient in Python and R. In my previous projects, I used Python for data manipulation and model building with libraries like Pandas and Scikit-learn, while R was used for statistical analysis and visualization.”

2. Can you describe your experience with deep learning frameworks?

This question assesses your familiarity with relevant tools.

How to Answer

Discuss your experience with frameworks like TensorFlow or PyTorch and specific projects where you utilized them.

Example

“I have extensive experience with TensorFlow and Keras for developing deep learning models. For instance, I built a convolutional neural network for image classification tasks, which involved preprocessing the data, designing the model architecture, and fine-tuning hyperparameters to achieve optimal performance.”

3. How do you ensure code quality and maintainability in your projects?

This question evaluates your coding practices.

How to Answer

Discuss best practices for code development, including documentation and testing.

Example

“I ensure code quality by following best practices such as writing clear and concise documentation, using version control systems like Git, and implementing unit tests to validate functionality. This approach not only improves maintainability but also facilitates collaboration with team members.”

4. What experience do you have with cloud computing platforms?

This question assesses your familiarity with modern computing environments.

How to Answer

Mention any experience with cloud platforms and how you have utilized them in your work.

Example

“I have experience using AWS for deploying machine learning models and managing data storage. I utilized services like S3 for data storage and EC2 for running computational tasks, which allowed for scalable and efficient processing of large datasets.”