Cash App Machine Learning Engineer Interview

This first round is a 30-minute call with the recruiter, mainly for them to get a sense of your background, what you’re looking for in your next role, and how that aligns with Cash App’s needs for an ML Engineer.

They'll typically cover your past work experience, especially any projects involving machine learning, model deployment, or large-scale data processing—key areas for ML at Cash App. They might also ask about your familiarity with frameworks like TensorFlow or PyTorch and your experience with AWS or similar cloud platforms since these tools are central to Cash App’s ML stack.

• Tell me a bit about yourself.
• Why CashApp?
• Which ML frameworks and libraries do you use most?
• How comfortable are you with cloud platforms like?
• Are there any specific areas within ML that you’re particularly excited to work on at Cash App?
• Can you give an example of a challenging problem you solved?

In the second round of the Cash App Machine Learning Engineer interview, you’ll go through a 45-minute Technical Screen. This round covers both high-level technical questions and a few behavioural ones. The technical problems here are practical and tend to align with data-heavy tasks you’d encounter in an ML role at Cash App—like data wrangling, working with large datasets, or implementing basic ML logic.

Here are three tips for this round:

• They’re looking for fluency in your preferred coding language, so be ready to dive into Python or whichever language you’re strongest in.
• Next tip, they'll want to see your debugging skills, so you might be asked to troubleshoot code in real-time or discuss ways to optimize an existing solution. Make sure you’re ready to explain why you’re choosing specific methods or optimizations, especially if the problem requires balancing accuracy and performance.
• Lastly, they’re also big on communication and collaboration. As you work through the solution, clearly explain your thought process so the interviewers are in loop.

In the third round of the Cash App Machine Learning Engineer interview, you’ll have a video call with the hiring manager. It’s meant to be a conversational session; expect to dive into the specifics of your past work—especially any machine learning projects that involve data-heavy applications, model training, or deployment. 

It’s less about technical grilling and more about understanding your working style, experience, and how you could contribute to their ML initiatives. They’re also looking to see if your background aligns with the kinds of projects Cash App’s ML team works on, so it’s a great opportunity to talk about any relevant experience with tools and frameworks like TensorFlow, PyTorch, or large-scale data processing.

Prepfully has a bunch of Cash App Machine Learning Engineers with a ton of experience who can coach you on how to approach this round.

In the onsite round for the Cash App Machine Learning Engineer interview, you’ll go through 4-5 interviews, ending with a wrap-up conversation with the hiring manager. Depending on whether you’re focusing on ML Modeling or ML Infrastructure, the interview structure will vary.

For ML Modeling candidates, the onsite includes a couple of coding sessions and case studies based on real-world problems the ML team at Cash App is working on. If you’re interviewing for an ML Infrastructure role, the focus shifts more toward architecture and system design.

Here's a deep dive:

Coding Interviews

ML Modeling candidates typically start with a couple of coding sessions. These are hands-on and tailored to Cash App’s real-world ML challenges—often focusing on data analysis, feature engineering, or model optimization. For instance, detecting patterns in transaction data—something you'd find with fraud detection or recommendations. 

Our most important tip here is to walk them through your entire ML pipeline; focus on why you’re making specific choices at each stage. They're interested in the why as much as the how. So, for example, explain why a certain feature selection technique works best for transaction data or why you’re choosing a specific model (e.g., gradient boosting vs. neural networks). Be clear about trade-offs, such as latency versus accuracy and how you'd handle that in a high-traffic environment.

Case Study

After the coding sessions, you’ll work on a case study that closely reflects the practical problems Cash App’s ML team tackles. Here, they want to see your approach to end-to-end problem-solving and how you manage trade-offs in real-world scenarios, so expect questions that test your ability to balance between accuracy and speed. For instance, when designing for anomalous behaviour detection, explain how you might optimise for quick detection with a light model, even if it means slightly lower accuracy, or vice versa.

Also, discuss your deployment strategy thoroughly. Cash App is big on reliable and scalable ML pipelines, so suggest best practices for safe deployment—like A/B testing models, gradual rollouts, or shadow deployments to minimise risk.

Pair Programming Session

This round typically pops up for ML Infrastructure candidates. It focuses on coding fluency but is less about algorithms and more about writing robust, production-quality code. The tasks are often around data ingestion or setting up scalable systems for model serving. For instance, designing a mini pipeline for data processing.

An important point here is that the objective is not just to solve the problem but to do so in a way that could easily be handed over to another engineer. So, focus on code readability and modularity—break down complex segments into smaller, easy to read and test parts. Then, make sure to walk the interviewer through your thought process and the reasoning behind your choices—whether it’s a specific data structure, framework, or handling edge cases. You can practise this interview with a Cash App Machine Learning Engineer for a guided 1-1 interview coaching session. Book a mock interviewer directly here.

The architecture and design interview digs into your approach to scalable ML infrastructure. You’ll likely have a whiteboarding session to design a system, such as a model deployment pipeline. Here, they look for your ability to build with reliability, versioning, and data privacy in mind. So, be prepared to discuss a couple of things. For instance, 

• How you’d ensure that the system can scale as demand grows while maintaining high uptime and minimal latency.
• How you’d implement version control for your models—such as tags or a centralized model registry
• What access control, encryption, or data masking techniques you’d apply to protect information during model deployment
• How you’d set up logging, metrics collection, etc

Past Technical Experience Interview

This interview is focused on your prior projects, especially those relevant to infrastructure and scalability. You’ll discuss your contributions to past systems, particularly in handling high-volume data or optimizing model-serving architecture. 

Cash App’s team looks for people who have experience with real-time data handling, version control, and scaling. So, be prepared to talk about relevant projects; think ahead and decide in advance which ones you'll talk about—such as where you've solved issues like latency reduction or set up monitoring and alerting systems in production, etc.

Wrap-Up with Hiring Manager

The onsite wraps up with a conversation with the hiring manager. This part is more informal where the HM will touch upon your long-term career goals, how you’d fit within the Cash App team, and your motivations for joining. They may also revisit some of your previous responses or ask how you’d approach broader challenges related to Cash App’s mission.

• Implement a function that finds the most frequent elements in a dataset. For example, given a list of predictions, find the top-k most frequent classes.
• Write a function to calculate the cosine similarity between two vectors.
• Implement a decision tree algorithm from scratch. Consider both binary classification and regression cases.
• Write a function that generates random samples from a normal distribution given a mean and standard deviation.
• Implement a function that calculates the entropy of a dataset.
• Implement a function to detect cycles in a directed graph.
• There’s a script that loads a large dataset, but it’s running out of memory. Work with your partner to modify the script to use generators or mini-batch loading to handle memory constraints.
• How would you design a recommendation system for an e-commerce website?
• How would you design a data pipeline to handle large amounts of real-time transaction data efficiently?
• What trends in machine learning and data security interest you, especially as they relate to financial technology?
• What excites you most about working with Cash App’s data and ML challenges?
• How would you design a feature store for ML models?
• How have you managed version control in past machine learning projects?
• How would you design a model deployment system that can handle an increase in traffic during peak times?
• Tell us about a project where you improved system scalability or optimised performance.