Company Overview

AMD (Advanced Micro Devices) is a technology company deeply committed to transforming lives with its innovative solutions. Known for its innovative approach to processors and graphics, AMD aims to create products that accelerate next-generation computing experiences, impacting various sectors including data centers, artificial intelligence, gaming, and PCs. The company's mission is underpinned by a culture that values execution excellence, diversity, inclusivity, and collaboration.

Machine Learning Engineer Position

The Machine Learning Engineer role at AMD is focused on optimizing and enabling AI applications for cutting-edge data centers and supercomputers. This position involves working with the latest ML frameworks, optimizing performance, and ensuring efficient deployment of AI models. Preferred candidates should have expertise in Python, C++, and experience with ML frameworks such as TensorFlow, PyTorch, and ONNX. The role requires strong problem-solving skills, collaboration with global teams, and a deep understanding of machine/deep learning concepts.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining AMD as a Machine Learning Engineer. Whether you were contacted by an AMD recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the AMD Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the AMD hiring manager remains present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. The technical screening for the AMD Machine Learning Engineer role usually is conducted virtually, including video conference and screen sharing. Questions in this 1-hour long interview stage may involve camera-based problem-solving like testing strategies and coding challenges. Specifically, you may expect questions on machine learning fundamentals, profiling ML workloads, and optimizing them for performance.

Depending on the team's requirement, you might receive a take-home assignment focusing on machine learning models. Apart from the coding and architectural questions, you will be evaluated on your problem-solving abilities and how you communicate and document your solutions.

Onsite Interview Rounds

Following a successful technical interview, you will be invited to attend the onsite interview loop at AMD. Multiple interview rounds, varying with the role, will be conducted during your day at the AMD office. During this phase, you could expect a blend of technical and behavioral interviews:

• Behavioral Interview: Here, the interviewers may dive deep into your past experiences, particularly focusing on how you resolve conflicts, your team collaboration skills, and how you align with AMD’s core values.
• Technical Interviews: These often involve whiteboard coding sessions, including data structures, algorithms, and specific machine learning problems. Also, you may need to demonstrate your expertise in Python and C++.
• Practical Exercises: You might need to present a previously given take-home assignment or solve real-time problems in a case study format. You could also discuss profiling and optimizing ML workloads or how you could enhance frameworks like PyTorch and TensorFlow.

Quick Tips For AMD Machine Learning Engineer Interviews

A few tips for acing your AMD interview include:

• Know Your AMD Products and Culture: AMD values innovation and solving the world's most critical challenges through technology. Understand AMD's products and services and how your role as a Machine Learning Engineer would contribute to their missions.
• Be Solution-Oriented: AMD looks for candidates who can think creatively to solve complex problems. Brush up on your machine learning fundamentals and be prepared to showcase scenarios where you’ve developed or optimized performance in ML models.
• Embody the Spirit of AMD: AMD champions inclusion, diversity, and collaboration. Reflect these values in your behavioral responses. Discuss times when you’ve worked with diverse teams and how you’ve leveraged different perspectives to drive success.

Typically, interviews at AMD vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Is this a fair coin given 8 tails and 2 heads in 10 flips? You flip a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance for a list of integers? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places.

• Is there anything suspicious about finding a significant variant in an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these findings.

• How do you find the median of a list with more than 50% of the same repeating integer in O(1) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in O(1) computational time and space.

• What are the drawbacks and formatting changes needed for student test score data in the given layouts? You have student test score data in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in "messy" datasets.

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might the number of job applicants be decreasing despite stable job postings? You observe that job postings per day have remained constant, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: What's the time complexity?

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain the process by which a random forest generates its forest. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network for a business problem and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier for emails? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track its accuracy and validity?

What is the average salary for a Machine Learning Engineer at Amd?

Q: What is the main role of a Machine Learning Engineer at AMD?

The primary role involves performance optimization of state-of-the-art machine learning workloads on AMD's CPU and GPU servers using open-source ML frameworks such as PyTorch, TensorFlow, and ONNX-Runtime. This includes developing and enhancing AMD's CPU inferencing product, ZenDNN, and enabling AI models, libraries, and applications for Instinct GPUs in both on-prem and Cloud environments.

Q: What qualifications and skills are required for a Machine Learning Engineer at AMD?

Candidates should have a strong background in machine learning fundamentals, including deep learning and large language models. Proficiency in programming languages like C++ and Python is essential. Additionally, experience with ML frameworks (PyTorch, TensorFlow), performance optimization, and production software quality assurance practices is required. A bachelor’s or master’s degree in Computer Science, Computer Engineering, or a related field is crucial, along with several years of related experience.

Q: What is the company culture like at AMD?

AMD is dedicated to transforming lives and industries with its cutting-edge technology. The company culture is centered around pushing the limits of innovation to solve significant global challenges. AMD values excellence in execution, being direct, humble, and collaborative, and inclusivity of diverse perspectives. The company's motto is "AMD together we advance."

Q: What is the interview process like for a Machine Learning Engineer role at AMD?

The interview process typically includes both behavioral and technical components. Candidates may face questions about their problem-solving abilities, technical expertise, and experiences related to machine learning and software development. Technical interviews may involve coding tests, discussions on testing strategies, and specific scenarios like optimizing ML workloads or working with distributed systems.

Q: How can I prepare for an interview for a Machine Learning Engineer position at AMD?

To prepare for an interview at AMD, focus on understanding the technical skills and experiences outlined in the job description. Practice common machine learning problems, coding challenges, and optimization scenarios using resources like Interview Query. Also, be ready to discuss your previous work, problem-solving approaches, and your understanding of the industry’s latest trends and technologies.

Conclusion

The role of a Machine Learning Engineer at AMD is an exciting opportunity to be part of revolutionary advancements in AI and machine learning. You'll be immersed in a dynamic team committed to driving innovation and enhancing global technology ecosystems. The comprehensive interview process, which includes behavioral questions and testing strategies, ensures that you get a complete understanding of the role and its day-to-day challenges.

If you want more insights about the company and prepare effectively for your interview, check out our main AMD Interview Guide. We cover many potential interview questions and provide interview guides for other roles, such as software engineer and data analyst.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every AMD machine learning engineer interview question and challenge.

Don't forget to explore all our company interview guides for better preparation. If you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!