Amgen Machine Learning Engineer Interview Questions + Guide in 2025

Amgen is a leading biotechnology company dedicated to transforming the lives of patients through innovative medicines and cutting-edge research.

As a Machine Learning Engineer at Amgen, you will play a pivotal role in leveraging data and advanced algorithms to drive insights that support the company’s mission of improving patient outcomes. You will be responsible for developing and implementing machine learning models that analyze complex biological data, enhance drug discovery processes, and optimize clinical trials. The key responsibilities include collaborating with cross-functional teams to identify data-driven opportunities, designing and executing experiments to validate model performance, and ensuring the scalability of solutions in a production environment.

To thrive in this role, you should possess strong programming skills in languages such as Python or R, along with a solid understanding of machine learning frameworks. Experience with data manipulation and analysis using tools like SQL, and familiarity with cloud technologies (AWS, Azure) is also essential. Additionally, exceptional problem-solving abilities, effective communication skills, and a collaborative mindset are traits that will make you a great fit at Amgen.

This guide will help you prepare for your interview by providing insights into the expectations for the role, the types of questions you may encounter, and how to align your responses with Amgen's values and business objectives.

The interview process for a Machine Learning Engineer at Amgen is structured and thorough, designed to assess both technical and interpersonal skills. Candidates can expect a multi-step process that includes several rounds of interviews, each focusing on different aspects of the role.

1. Initial Screening

The process typically begins with an initial phone screening conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, experiences, and motivations for applying to Amgen. The recruiter will also assess your fit for the company culture and may inquire about your need for visa sponsorship.

2. Technical Assessment

Following the initial screening, candidates may be required to complete a technical assessment. This could take place on platforms like HackerRank and generally includes coding challenges that test your proficiency in programming languages relevant to machine learning, such as Python or SQL. The assessment may also cover statistical concepts and machine learning algorithms.

3. Managerial Interview

After successfully completing the technical assessment, candidates typically have a phone interview with the hiring manager. This interview focuses on your technical skills, project experience, and how your background aligns with the team's needs. Expect questions that delve into your understanding of machine learning concepts and your approach to problem-solving.

4. Panel Interviews

The final stage usually consists of multiple panel interviews, often conducted in a single day. Candidates will meet with various team members, including engineers and possibly higher-level management. Each interview lasts around 30-45 minutes and includes a mix of technical and behavioral questions. Interviewers will assess your ability to work collaboratively, handle project challenges, and communicate effectively.

5. Onsite Visit (if applicable)

In some cases, candidates may be invited for an onsite visit, where they can meet the team in person and get a feel for the work environment. This visit may include additional interviews and a tour of the facilities.

Throughout the process, candidates should be prepared for a variety of questions that assess both their technical expertise and their ability to work within a team.

Next, let's explore the specific interview questions that candidates have encountered during the process.

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

Understand the Interview Structure

Amgen's interview process often includes multiple rounds, starting with a phone screening followed by technical assessments and panel interviews. Familiarize yourself with this structure to manage your time and energy effectively. Prepare for a variety of interview formats, including one-on-one sessions and group panels, as you may encounter both during your interviews.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process at Amgen. Be ready to discuss your past experiences in detail, particularly those that demonstrate your problem-solving skills, teamwork, and ability to handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples that highlight your qualifications for the role.

Showcase Your Technical Skills

As a Machine Learning Engineer, you will likely face technical questions related to machine learning algorithms, data structures, and programming languages such as Python and SQL. Brush up on your technical knowledge and be prepared to solve coding problems on the spot. Practice common coding challenges and familiarize yourself with the tools and technologies relevant to the role.

Emphasize Cross-Functional Collaboration

Amgen values candidates who can work effectively across different teams and departments. Be prepared to discuss your experience in cross-functional projects and how you have successfully collaborated with diverse teams. Highlight your communication skills and ability to navigate complex organizational structures, as this will resonate well with the interviewers.

Research the Company Culture

Understanding Amgen's culture is crucial for your interview success. The company emphasizes innovation, collaboration, and a commitment to improving patient outcomes. Familiarize yourself with their mission and values, and be ready to articulate how your personal values align with those of the company. This will demonstrate your genuine interest in the role and the organization.

Prepare Thoughtful Questions

At the end of your interview, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Ask about team dynamics, ongoing projects, or the company's future direction. Thoughtful questions can leave a positive impression and show that you are engaged and proactive.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity to interview. This is not only courteous but also reinforces your interest in the position. Use this opportunity to briefly reiterate your enthusiasm for the role and how you can contribute to the team.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Amgen. 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 Amgen. The interview process will likely assess your technical skills in machine learning, programming, and data analysis, as well as your ability to work collaboratively in a team environment. Be prepared to discuss your past experiences, problem-solving approaches, and how you can contribute to Amgen's mission.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. Highlight the scenarios in which you would use one over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

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.

How to Answer

Discuss a specific project, focusing on the problem you were solving, the approach you took, and the challenges you encountered, along with how you overcame them.

Example

“I worked on a project to predict patient outcomes based on historical health data. One challenge was dealing with missing data, which I addressed by implementing imputation techniques. This improved the model's accuracy significantly.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model evaluation and optimization.

How to Answer

Explain the concept of overfitting and discuss techniques you use to prevent it, such as cross-validation, regularization, or pruning.

Example

“To handle overfitting, I often use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 or L2 to penalize overly complex models.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

Discuss various metrics relevant to the type of model you are working with, such as accuracy, precision, recall, F1 score, or AUC-ROC.

Example

“I typically use accuracy for classification tasks, but I also consider precision and recall, especially in imbalanced datasets. For regression tasks, I look at metrics like RMSE and R-squared to assess model performance.”

Programming and Technical Skills

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

This question assesses your technical skills and experience.

How to Answer

Mention the programming languages you are comfortable with, and provide examples of how you have applied them in your work.

Example

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

2. Can you explain how you would optimize a machine learning model?

This question evaluates your understanding of model optimization techniques.

How to Answer

Discuss various strategies for optimization, including hyperparameter tuning, feature selection, and algorithm selection.

Example

“To optimize a machine learning model, I would start with hyperparameter tuning using techniques like grid search or random search. Additionally, I would analyze feature importance to eliminate irrelevant features, which can improve model performance.”

3. Describe your experience with SQL and how you have used it in data analysis.

This question assesses your data manipulation skills.

How to Answer

Explain your familiarity with SQL and provide examples of how you have used it to extract and analyze data.

Example

“I have extensive experience with SQL for querying databases. In a previous role, I used SQL to extract patient data from a relational database, which I then analyzed to identify trends in treatment outcomes.”

4. How do you ensure the quality and integrity of your data?

This question tests your understanding of data quality management.

How to Answer

Discuss the methods you use to validate and clean data, ensuring it is suitable for analysis.

Example

“I ensure data quality by implementing validation checks during data collection and using data cleaning techniques to handle missing or inconsistent data. Regular audits and cross-referencing with reliable sources also help maintain data integrity.”

Behavioral and Situational Questions

1. Describe a time when you had to work collaboratively on a project. What was your role?

This question assesses your teamwork and collaboration skills.

How to Answer

Provide a specific example of a collaborative project, detailing your contributions and how you worked with others.

Example

“I collaborated with a cross-functional team to develop a predictive model for patient readmissions. My role involved data analysis and model development, and I facilitated communication between data scientists and healthcare professionals to ensure the model met clinical needs.”

2. How do you prioritize tasks when working on multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any tools or methods you use to manage your workload.

Example

“I prioritize tasks based on deadlines and project impact. I use project management tools like Trello to track progress and ensure that I allocate time effectively to meet all project requirements.”

3. Tell me about a time you faced a significant challenge at work. How did you handle it?

This question assesses your problem-solving abilities and resilience.

How to Answer

Describe a specific challenge, your approach to resolving it, and the outcome.

Example

“I faced a challenge when a key dataset was corrupted just before a project deadline. I quickly coordinated with the data engineering team to recover the data and implemented a backup strategy to prevent future occurrences. We managed to meet the deadline with minimal disruption.”

4. Why do you want to work at Amgen?

This question gauges your motivation and alignment with the company’s values.

How to Answer

Express your interest in Amgen’s mission and how your skills align with their goals.

Example

“I am drawn to Amgen’s commitment to innovation in biotechnology and its focus on improving patient outcomes. I believe my background in machine learning can contribute to developing solutions that enhance healthcare delivery.”