Insider tips for AI & machine learning interviews

As recruiters embedded in Capital One’s AI and machine learning hiring teams, Katie, Chris and Vaishali have spent years helping candidates land roles in this cutting-edge space. They act as the first call and a steady partner through an interview process designed to match the depth and speed of the technology we build. They know what hiring managers look for, what trips candidates up and what it actually takes to stand out—and they’re ready to share their insider tips.

Here’s what they want you to know.

What to expect from your AI and ML interview at Capital One

The AI and ML interview process at Capital One moves in clear stages, and understanding the shape of it matters as much as any technical prep. After an initial recruiter screen, candidates complete an algorithmic coding assessment—70 minutes, four questions, all based on data structures and algorithms. Think LeetCode. 

Vaishali’s advice for the coding assessment is simple: Only start it when you feel ready. 

“You get 10 days to complete it,” she said. “But once you start, you have to finish in that session.” If it’s been a while since you’ve written production-level Python, spend a few days brushing up on your skills, and don’t rush.

Chris often reassures candidates by reminding them that the assessment is pass/fail. “We’re not trying to weed people out from the start. The purpose is to make sure you have a basic fundamental understanding of how to write code.”

From there, candidates move through a resume review and a 30-minute interview with the hiring manager before reaching what the team calls Power Day: four hour-long interviews covering technical coding, system design, behavioral interviews and a case study.

Power Day is a conversation, not a cross-examination

Of all the things that surprise candidates about Capital One’s interview process, the collaborative nature of Power Day tends to catch people off guard the most.

The format is intentionally designed to mirror a real work day. Interviewers are not there to watch you struggle. They’ll ask clarifying questions, drop hints and redirect you if you veer off track. What they’re looking for is how well you incorporate feedback in real time.

The first technical interview splits into two halves: 30 minutes of live coding in CodeSignal, the same platform used for the initial assessment, followed by 30 minutes where the interviewer goes deep on your AI/ML skills and how they connect to what the team is building. Then the second technical interview shifts to system design, where candidates are asked to develop a full end-to-end infrastructure built on modern LLM concepts.

“The specific team that they’re interviewing for is going to dictate the actual questions that come up,” Chris said. Interviewing for a platform role? Brush up on ethical guardrails and LLM safety. An inferencing position? Know your LLM inferencing techniques cold. Agentic, pre-training, fine-tuning—the content shifts with the team. It’s always worth asking your recruiter exactly which team you’ll be meeting with before Power Day so you can prepare accordingly.

The behavioral and case interviews round out the day and are the same across AI and ML roles. For behavioral interviews, the STAR method is used, where you’ll be asked to break down your examples by situation, task, action and result. Katie points out a critical nuance candidates often overlook: Capital One is looking for people who think holistically.

“We want people that are going to ask a second layer of questions,” she said. “That might mean asking, ‘Have we considered X, Y and Z as another approach?’ Showing that you can explore multiple ideas and solutions and think critically matters just as much as showing that you can deliver.”

For the case interview, you don’t need a financial background. The case is built around Capital One products that are publicly available, and the interviewers are far more interested in how you think through a problem than whether you already know the answer.

Vaishali notes that all four rounds carry equal weight in the final consensus. A gap in one area can be offset by a demonstrated growth mindset in another.

“I recently worked with a candidate whose initial feedback wasn’t a unanimous yes,” Vaishali said. “But during the behavioral round, they won the team over by being completely honest about what they didn’t know, then showing a strong drive to learn quickly. The hiring manager was so impressed by their drive and growth mindset that they told me to get them on the team right away.”

What sets a strong AI and ML candidate apart

The candidates who thrive at Capital One are the ones who are genuinely curious and know how to collaborate.

“Intellectual curiosity really separates people,” Chris said. “The AI space is constantly changing. The people who are most successful are those who are staying up as new papers get published and are fluid in how they adapt to new AI tools and techniques.”

Katie has seen candidates who came in a little light on actual work experience but won over the room by talking about something they’d built on their own time. “That stood out as someone who wants to learn something new.” 

On the flip side, one of the most common stumbling points has nothing to do with technical AI and ML skills. When interviewers ask a probing follow-up about a project, candidates who can only describe what happened, and not why, tend to struggle. Ownership is everything. If your team solved a hard problem, tell that story in a way that makes your specific contribution clear.

One resource AI and ML candidates consistently overlook

All three recruiters agree about the one resource candidates consistently underuse: their recruiter.

Capital One’s AI and ML recruiters are embedded in the business. They talk to hiring managers daily. They attend academic and industry conferences like ICML and NeurIPS to connect with the research community. They know which teams are hiring, and what problems those teams are trying to solve. That’s not information you can get from a job description.

“Ask us questions about the people you’re going to be talking to,” Katie said. “Ask for our tips. We’ll give you as much information as we have.”

For Vaishali, building connections is personal. When schedules allow, she loves giving local candidates the opportunity to visit Capital One before they apply. Almost every candidate she invites takes her up on it, leaving with a genuine, firsthand feel for our culture and environment.

“They often ask, is it actually like this?” Vaishali said, laughing. “And I say yes, come on in.”

Curious what a career in AI and ML at Capital One could look like? Browse open roles and start a conversation.