What is a Machine Learning Engineer at Appfolio?

As a Machine Learning Engineer at Appfolio, you are at the forefront of transforming the real estate and property management industry through artificial intelligence. Appfolio builds powerful cloud-based business management software, and our machine learning teams are responsible for injecting intelligence into every layer of this ecosystem. You will be tackling complex problems involving vast amounts of property, financial, and user data to automate workflows, optimize pricing, and improve the leasing experience for millions of users.

The impact of this position is massive. You are not just building models in a vacuum; you are directly influencing core product features that save our customers thousands of hours. Whether it is leveraging deep learning to extract data from complex financial documents, building recommendation systems for prospective tenants, or predicting maintenance issues before they occur, your work will have high visibility and immediate business value.

Expect a highly collaborative, fast-paced environment where you will balance rigorous academic machine learning concepts with practical software engineering. This role requires a unique blend of theoretical depth—particularly in neural networks and deep learning—and the engineering pragmatism needed to deploy scalable solutions into production. You will work alongside passionate engineers and product managers to define the future of property technology.

Common Interview Questions

Getting Ready for Your Interviews

Preparation is the key to navigating the technical rigor and behavioral depth of our hiring process. We evaluate candidates holistically, looking for a strong foundation in machine learning theory, hands-on coding proficiency, and alignment with our collaborative culture.

Focus your preparation on these key evaluation criteria:

Deep Learning and ML Fundamentals You must demonstrate a deep understanding of core machine learning concepts and neural network architectures. Interviewers will evaluate your ability to explain the mathematical intuition behind models, select the right algorithms for specific problems, and discuss trade-offs in model design and performance metrics.

Applied Coding and Implementation Theoretical knowledge must translate into working code. We evaluate your hands-on ability to write clean, efficient Python code and implement deep learning models using modern frameworks. Strong candidates can comfortably code neural network components from scratch or quickly prototype solutions while managing edge cases and optimizing for performance.

Systematic Problem Solving We look for engineers who can take ambiguous, real-world business problems and translate them into structured machine learning tasks. You will be assessed on how you frame problems, validate your assumptions, structure your data pipelines, and iterate on your solutions when initial approaches fail.

Culture Fit and Communication At Appfolio, how you work is just as important as what you build. We evaluate your ability to communicate complex technical concepts clearly, collaborate with cross-functional teams, and handle feedback. Demonstrating passion for our mission and a resilient, adaptable mindset will strongly differentiate you.

Interview Process Overview

The interview process for a Machine Learning Engineer at Appfolio is designed to be comprehensive, challenging, and fair. The entire recruitment cycle typically takes about one month from application to final decision. You will begin with an initial recruiter phone screen, where you will discuss your background, role responsibilities, and high-level alignment. Our recruiting team prides itself on exceptional responsiveness and transparency, ensuring you are supported and informed at every step.

Following the recruiter screen, you will move to a technical phone screen or initial virtual technical round. This stage heavily emphasizes machine learning concepts and often includes hands-on deep learning coding exercises. If successful, you will advance to the virtual onsite stage. The onsite structure can vary slightly depending on the specific team and seniority of the role; it may consist of two intensive 1-hour sessions (one behavioral with a hiring manager, one technical with a Staff Engineer) or expand into a more rigorous loop of up to five technical rounds and one behavioral round.

Throughout the process, you will meet with highly knowledgeable and passionate team members. The technical discussions are known to be rigorous but fair, testing both your conceptual depth and your practical coding skills.

This visual timeline outlines the typical sequence of your interview journey, from the initial recruiter touchpoint through the technical screens and the final virtual onsite loop. Use this to pace your preparation—focusing heavily on deep learning coding and core concepts early on, and shifting toward system design, behavioral narratives, and cross-functional communication as you approach the onsite stage. The variation in the onsite loop means you should build the stamina for multiple hours of technical deep dives.

Deep Dive into Evaluation Areas

To succeed, you must excel across several distinct technical and behavioral dimensions. Our interviewers will probe deeply into your past experiences and your ability to solve novel problems.

Machine Learning Concepts and Theory

This area tests your foundational knowledge of machine learning. Interviewers want to see that you understand the "why" behind the algorithms, not just how to import a library. Strong performance means you can discuss the mathematical underpinnings of models, explain bias-variance trade-offs, and justify your choice of loss functions or optimization algorithms.

Be ready to go over:

• Supervised vs. Unsupervised Learning – Knowing when to apply which techniques and how to evaluate them.
• Model Evaluation Metrics – Precision, recall, F1-score, ROC-AUC, and how to choose the right metric for imbalanced datasets.
• Optimization Algorithms – Gradient descent variants (Adam, RMSprop, SGD) and how they impact convergence.
• Advanced concepts – Regularization techniques (L1/L2, dropout), hyperparameter tuning strategies, and handling data drift.

Example questions or scenarios:

• "Explain the difference between bagging and boosting, and give an example of when you would use each."
• "How do you handle a dataset with highly imbalanced classes?"
• "Walk me through the mathematical intuition behind backpropagation."

Deep Learning and Hands-On Coding

Because deep learning is heavily utilized at Appfolio, you will face dedicated coding rounds focused on neural networks. You are evaluated on your fluency in Python and your ability to implement models using frameworks like PyTorch or TensorFlow. A strong candidate writes modular, bug-free code and can confidently debug network architectures on the fly.

Be ready to go over:

• Neural Network Architecture – Designing MLPs, CNNs, or RNNs/Transformers depending on the data modality.
• Framework Fluency – Writing custom training loops, defining custom layers, and managing tensors in PyTorch or TensorFlow.
• Data Pipelines – Efficiently loading, augmenting, and batching data for training.
• Advanced concepts – Implementing specific layers from scratch (e.g., self-attention) or debugging vanishing/exploding gradients in live code.

Example questions or scenarios:

• "Write the code to build and train a simple feedforward neural network from scratch using PyTorch."
• "Implement a custom loss function that heavily penalizes false negatives."
• "Given this dataset, code a data loader that applies dynamic augmentation during training."

Behavioral and Experience Deep Dive

Technical brilliance must be paired with operational maturity. In rounds with hiring managers and cross-functional partners, you will be evaluated on your past impact, your leadership qualities, and how you navigate workplace challenges. Strong performance involves telling clear, structured stories that highlight your specific contributions and your ability to learn from failures.

Be ready to go over:

• Project Impact – Discussing the end-to-end lifecycle of a model you deployed and its business outcome.
• Navigating Ambiguity – How you proceed when requirements are unclear or data is messy.
• Collaboration – Examples of working with product managers, data engineers, or non-technical stakeholders.
• Advanced concepts – Mentoring junior engineers, driving architectural decisions, and advocating for ML best practices within an organization.

Example questions or scenarios:

• "Tell me about a time a machine learning model you deployed failed in production. How did you diagnose and fix it?"
• "Describe a situation where you had to explain a complex technical trade-off to a non-technical stakeholder."
• "Walk me through the most challenging deep learning project you have led from conception to deployment."