Winning Strategies to Ace Your AI Product Manager Interview

If you are booked for an AI product manager interview, you have secured a significant—probably one-off—chance to impress the company. 

Though product management interviews and hiring processes can be different, there are efficient ways you can prepare for them if you know what to expect. 

As artificial intelligence and machine learning models continue to extend their use cases to the daily needs of the general public, AI product management positions become more and more in demand. 

So, how can you ace your AI product manager interview and eventually land the job? 

How to Ace the AI Product Manager Interview

#1: Understand the AI Product Manager Role

There is traditional product management, and there’s AI product management.

Being in the AI sector of this industry means playing a part in the creation, development, and rollout of products powered by machine learning technology.

An AI product management role is most likely not an entry-level position. For the most part, successfully hired AI product managers have several years of experience either as a product manager or working in AI/ML

What Does an AI Product Manager Do?

An AI Product Manager:

• defines product requirements, vision, and roadmap for an AI-based product;
• develops data and training model strategy,
• prioritizes machine learning-related metrics of success for better user experience, technical feasibility, and business value;
• ensures AI products adhere to ethical standards, taking transparency, fairness, and privacy into consideration;
• collaborates with data scientists and software engineers to understand artificial intelligence capabilities;
• communicates with stakeholders, addressing their concerns and managing their expectations;
• and is responsible for other industry-specific AI integrations.

What Skills Does an AI Product Manager Need?

The skills that an AI Product Manager would need to have and demonstrate in the interview are broken into three broad categories:

1. Technical Skills

2. Product Management Skills

Strategic thinking, problem-solving, and organizational strength are some of the skills that a PM uses throughout the product-building process.

In AI product management, these skills become even more indispensable as there are probably more challenges involved compared to developing most software products.

First, building AI products can be resource intensive. As the product team develops a roadmap, strategic planning is essential in deciding which features and enhancements to prioritize, depending on user needs, product goals, and feasibility.

AI product managers also need to consider data quality, regulatory compliance, and potential performance degradation, not to mention AI products being highly iterative.

3. Soft Skills

Soft skills like communication, leadership, and customer empathy also make up an ideal AI product manager hire.

Being an AI PM means collaborating with technical teams, communicating with stakeholders, and managing users’ expectations.

#2: Learn the Different Types of AI PM Interview Questions

AI product management interviews can be intense. But, if you are familiar with the questions they’re going to ask, you can prepare for them ahead of time.

While the exact questions are difficult to predict, these questions usually fall into five categories:

1. AI Technical Knowledge Questions

How much do you understand the technology that enables artificial intelligence? They will attempt to gauge your knowledge of data science, generative AI, GPTs, machine learning algorithms, and their real-world applications.

While you do not need to be a coder, you need to understand data collection and training, how inference works, types of model architecture and their use cases, validation and testing, and deep learning among other concepts of AI.

These questions can look like the following examples:

1. How do you measure the success of a cancer diagnostic model that accepts MRI images as input and detects whether or not a patient has cancer?
2. How would you finetune a trained model?
3. When would you use deep learning instead of a simpler machine learning model?

2. Questions About Your Experience

Questions concerning your prior work experience with AI projects and collaborating with other industry specialists will surely arise.

You may encounter general questions about your work background, or the interviewer is going to ask you about a specific challenge you’ve encountered in one of the previous employments listed in your resume.

Knowing that this is an AI product manager role, demonstrate your AI-related technical experience, as well as the PM and soft skills you were able to develop in the past.

If you’re an aspiring AI product manager yet lack sufficient experience in the field, consider doing these during your free time:

• building an ML product;
• attending hackathons;
• or joining machine learning training programs (and getting a certificate)—

all of which you can add to your resume and discuss with the interviewer as valuable technical experience in AI/ML.

Here are a few examples of experience questions in an AI product manager interview:

1. Which machine learning models have you worked with, and why did you agree to use them from a product management perspective?
2. Explain the data pipeline for the last AI project you worked on. What were the top challenges in getting data, and how did you resolve them?
3. Can you give an example of a project in which you had to balance AI innovation with regulatory compliance and privacy concerns?

3. Product Vision and Strategy Questions

An ideal AI PM candidate can identify problems that state-of-the-art artificial intelligence products can solve.

To assess that, you may be asked hypothetical, or case study questions related to product vision and strategy.

These questions can be complex, and the best way to prepare for them is through frequent practice.

You may choose sample AI product strategy questions, and then answer them in a mock interview with a peer to assess your performance. You can also record yourself and review your response to determine areas of improvement.

Below are a few examples of product vision and strategy questions:

1. If you were in charge of Walmart, how would you have used GenAI to improve the customer experience?
2. How do you see product management daily tasks evolve with AI-first tools?
3. What should Uber’s mobile app strategy be given recent LLM developments? 

To answer product vision and strategy questions, you may use a proven problem-solving framework. Here is a sample framework for AI-based questions:

1. Define the problem.
2. Identify product goals (including non-goals, focusing on how users can benefit from the product).
3. Specify the top metrics to measure success.
4. Define the user journey.
5. Determine the pain points throughout the user journey.
6. Prioritize the pain points (based on impact and frequency).
7. Decide if you’re going to use a rule-based or machine-learning-based model.
8. Propose solutions (addressing the pain points).
9. Prioritize solutions (based on effectiveness, engineering effort, and cost).
10. If an ML solution is preferred, scope the solution to solve user needs.
11. Define the new user journey (based on the solution).
12. Define the MVP features and roadmap (future plans).

4. Situational, Leadership, and Empathy Questions

Interviewers will still want to gauge your skills in decision-making, collaboration, and user empathy. Will you be able to identify unique opportunities to stand out in the market and solve them using the new foundational technologies that are becoming available to AI product managers?

Since AI product management involves cross-functional collaboration, communication and leadership skills are critical. Describe your experience leading a team to success and mediating conflicts in a complex technical setting.

Plus, presenting your ability to be mindful of responsible AI is a significant factor in every company. As an aspiring AI PM, you need to recognize possible bias, privacy, and transparency issues, and how to avoid them.

We have seen instances where consumers felt like their information was unsafe, or a generative AI product’s decisions favor one group of people over another.

Knowing the best practices for responsible AI product development can help you ace situational questions in your interview. 

In doing so, master your storytelling and provide real-life examples.

5. Operational and Project Management Questions

Operational and project management responsibilities will remain to be an important part of product development for AI-first products.

As an AI product manager, how do you plan on carrying out data deployment, responsible AI risk management, rapid iteration on the models, inference performance measurement, and product monitoring among other operational tasks?

As you might already know, building a working ML model is a highly iterative process. Likely, the first version of your model won’t work, and you will have to iterate based on the results of the experiments and feedback from the rest of your team.

Continuous monitoring and optimization are essential to address issues such as model drifting, which is what happens when the model’s predictions lose accuracy over time.

As an AI product manager, you need to plan for regular evaluations and updates to maintain and improve model performance. You want to be alerted as soon as the quality begins to decline.

Some of the examples of operational and project management questions that you may encounter include:

1. How do you handle model accuracy vs. delivery speed when shipping new ML models?
2. How do you know how often you should retrain your model?
3. Could you give an instance of a risk you ran across in deploying an AI product even though your compliance team was concerned from a responsible AI perspective? How did you mitigate this risk?

#3: Get to Know Your Product

Do your diligent research and be familiar with the product you’re interviewing for.

Considering that it’s an AI product, a sufficient understanding of the problem it solves, the sources of its data, its training and inference processes, its model architecture, and its long-term strategic vision is essential.

Aside from these insights, get more information about the product’s:

A. Current Operation and Challenges

Study the product’s history and current user needs that it is trying to enable by leveraging AI or machine learning. Try to understand how they leverage machine learning so you can understand the challenges and opportunities better. This will help you better talk about the product roadmap.

Your research should cover insights about the product’s tech stack, MLOps strategy, and current initiatives, including internal and external ones.

You will also want to know who their competitors are. Conduct a competitor analysis to craft a better vision for the product, one that will make it stand out.

Additionally, come up with suggestions on how to monitor the performance of the models that enable unique product features. This will help you ask more targeted questions and engage in more meaningful conversations with them.

B. Ethical and Legal Considerations

There is a collection of ethical factors that AI products are facing. Some examples are:

• Interpretability
• Bias/Fairness
• Trustworthiness
• Security
• Privacy
• Transparency

Prior to your interview, study the ethical factors and regulations that govern the industry in which the product operates. For instance, AI products in healthcare, finance, education, and government are facing a set of ethical concerns that are specific to their industries.

An AI product manager needs to be familiar with these considerations to avoid causing harmful impacts on the users and the business down the road, which may also lead to legal conflicts.

Bias

Let’s take bias, as an example.

One of the major risks involved in building an AI product is producing output that’s discriminatory around age, race, gender, religion, socioeconomic status, and more.

In your interview, you need to show how you can address bias-related concerns as an AI product manager.

Where does bias come from? Historically, humans have developed biases within our society. For instance, there is evidence of gender bias in considering potential candidates for certain jobs in the past.

Machine learning models can pick up on such biases if the dataset is skewed or certain demographics have been historically neglected.

AI PMs need to lead the task of addressing bias within the product to avoid or mitigate ethical and legal concerns.

Sadly, scrubbing the data of the favored or discriminated group isn’t enough since there are other correlated variables involved.

What can an AI product manager do to minimize bias? Here is an example of how you can address bias:

1. Measure the bias.

Machine learning models are made of neural networks that associate words with numbers to make calculations and predictions. This numeric representation is called word embeddings.

To measure the bias, you can perform a word embedding association test (WEAT) to determine which group of words is more likely to be associated with one another to cause bias.

E.g.,

• Male: career, executive, leadership
• Female: family, domestic, caregiver

Machine learning tools like Amazon SageMaker and IBM AI Fairness can facilitate bias measurement.

However, when it comes to your product’s specific use case, it’s more cost-effective to build your own set of metrics. This way, you can be more confident in addressing the existing bias within your dataset.

2. Mitigate the bias.

While it’s difficult to completely eliminate bias in building AI products, there are effective ways to mitigate it. You can do that within the three stages of product development:

• before training,
• during training,
• and after deployment.

Processing your dataset before training to measure and address bias is an effective way to ensure fair representation.

You can do this by collecting more data, augmenting the data, or using synthetic data from other generative AI products to achieve proportion.

During model training, undersampling/oversampling is usually done to balance the dataset. Adversarial debiasing technique is also an option, where you train the model to disregard sensitive features.

Amidst training, you can also perform the human-in-the-loop review process and put-up guard rails, such as a disallow list, user constraints, and UI disclaimers.

Product managers are usually in control of laying down product constraints. This can be a tough responsibility for PMs since lowering risks may also lead to lower utility.

BONUS TIP: Prepare Insightful Questions

Before the interview, create a list of questions for the interviewer that demonstrate your strategic thinking. As you do so, consider the AI product’s potential impact on the users’ experience and the company’s future.

You may read AI product management case studies to get ideas on what to ask. In our AI product management learning program, we go through AI product case studies to gain insight into their development from a product management perspective.

#4: Engage in a Mock AI Product Manager Interview

Now that you have the technical background and AI experience, how confident are you in answering interview questions?

You can get a better idea of this by practicing in a mock interview.

Ask a friend or a mentor to help you out and rehearse your responses effectively. Ideally, you’d want to practice with someone who is also preparing for AI PM interviews or working as an AI PM so they can give you meaningful feedback.

PM Exercises provides you with the opportunity to match with aspiring or existing PMs for mock interviews and group practice sessions.

We are also offering a 4-week AI Product Management Learning Cohort led by expert AI PMs working at some of the largest companies in the world. Aside from the technical fundamentals of AI and machine learning, our instructors will teach you how to answer AI product management interview questions and build an AI-first product.

Common Interview Questions for AI Product Managers:

1. Can you describe your experience designing AI-powered products?
2. How do you ensure the AI product you’re managing is ethically designed and used?
3. Can you describe an instance when you had to adjust your AI product strategy based on customer input or market changes?
4. How do you keep up with AI technologies and advancements?
5. Can you give an example of a project in which teamwork was critical to generating successful results within an AI context?
6. How do you assess the performance and success of an ML model?
7. What are the most important factors to consider when incorporating AI into existing products?
8. How did you get into AI technology—by chance or actively seeking out a career in the field?
9. How might artificial intelligence influence the types of products we create in the future?
10. How do you ensure the responsible use of AI in your product, considering ethical considerations and regulations?

Access Proven PM Interview Support

Acing your AI product manager interview requires a strategic approach, and having the right mentors behind your back is a step in the right direction.

I founded PM Exercises with some of the world’s most seasoned PMs to help prospective product managers in their search for a fulfilling career in this sector.

Aside from accessing our collection of interview questions, general PM courses, and PM community, you can also join our exclusive AI Product Manager Learning Program and train with AI PM instructors coming from Uber and Google.

Check out more details here.