Move ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Move? The Move Machine Learning Engineer interview process typically spans technical, analytical, and product-focused question topics, evaluating skills in areas like applied machine learning, data engineering, system design, and communicating complex insights to both technical and non-technical stakeholders. Interview preparation is essential for this role at Move, as candidates are expected to demonstrate not only expertise in building scalable ML solutions and handling large datasets, but also the ability to contextualize their work within Move’s commitment to innovative, data-driven decision-making in transportation and mobility services.

In preparing for the interview, you should:

• Understand the core skills necessary for Machine Learning Engineer positions at Move.
• Gain insights into Move’s Machine Learning Engineer interview structure and process.
• Practice real Move Machine Learning Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Move Machine Learning Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Move is a technology company focused on revolutionizing digital payments and financial infrastructure through secure, scalable solutions. The company develops modern payment rails and APIs that enable seamless money movement for businesses and developers, emphasizing speed, reliability, and compliance. Move’s mission centers on building open, accessible financial systems that empower innovation in fintech. As an ML Engineer, you will contribute to enhancing fraud detection, risk management, and transaction efficiency, directly supporting Move’s commitment to safe and efficient financial operations.

As an ML Engineer at Move, you will design, develop, and deploy machine learning models to solve real-world business challenges and enhance the company’s technology offerings. You will work closely with data scientists, software engineers, and product teams to build scalable solutions that improve product features and drive user engagement. Your responsibilities may include preprocessing large datasets, selecting appropriate algorithms, optimizing model performance, and integrating models into production systems. This role is key to advancing Move’s data-driven initiatives and ensuring the reliability and impact of its machine learning applications within the company’s product ecosystem.

2.1 Stage 1: Application & Resume Review

The initial step involves a thorough screening of your application materials, with a focus on demonstrated experience in designing and deploying machine learning solutions, handling large-scale datasets, and integrating ML models into production environments. Hiring managers and technical leads will look for evidence of proficiency in Python, SQL, data cleaning, feature engineering, and experience with cloud-based ML infrastructure. Highlighting past projects involving scalable model development, ETL pipelines, and impactful business outcomes will set your application apart.

2.2 Stage 2: Recruiter Screen

This stage typically consists of a 30-minute phone or video conversation with a recruiter. The discussion centers on your motivation for joining Move, your understanding of the company’s mission, and your alignment with the ML Engineer role. Expect to briefly summarize your background, technical expertise, and communication skills, as well as clarify any details from your resume. Preparation should include a concise career narrative and clear articulation of your interest in Move and machine learning engineering.

2.3 Stage 3: Technical/Case/Skills Round

You will be invited to participate in one or more technical interviews, which may be conducted virtually or in-person. These sessions are led by senior ML engineers or data scientists and focus on your ability to solve real-world ML problems. You’ll be asked to tackle case studies involving data cleaning, model selection, feature store integration, system design for scalable ML solutions, and coding exercises in Python or SQL. Expect to discuss the tradeoffs between model accuracy and speed, approaches to regularization and validation, and strategies for deploying ML models at scale. Preparation should involve reviewing core ML algorithms, system design principles, and demonstrating hands-on coding proficiency.

2.4 Stage 4: Behavioral Interview

The behavioral round is typically conducted by a mix of technical team members and hiring managers. This interview explores your ability to collaborate across teams, communicate complex insights to non-technical stakeholders, and navigate project challenges. You’ll be asked to reflect on past experiences leading data projects, handling ambiguous requirements, and exceeding expectations. Prepare to discuss your strengths and weaknesses, adaptability, and how you’ve contributed to a positive team culture.

2.5 Stage 5: Final/Onsite Round

The final stage often consists of a series of onsite or extended virtual interviews with cross-functional team members, including engineering managers, product owners, and senior leadership. You may encounter deeper technical challenges, system design scenarios (such as building a digital classroom or designing a retailer data warehouse), and business case discussions. This round assesses your holistic fit for Move’s fast-paced, impact-driven environment, as well as your ability to present and justify ML solutions to diverse audiences.

2.6 Stage 6: Offer & Negotiation

Once all interviews are complete, the hiring team will convene to assess your performance across all rounds. If successful, you’ll receive an offer from the recruiter, which includes compensation details, benefits, and role expectations. You’ll have the opportunity to negotiate terms and clarify any remaining questions about the team, projects, and career growth at Move.

2.7 Average Timeline

The typical Move ML Engineer interview process spans 3-4 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may progress in as little as 2 weeks, while standard timelines allow for scheduling flexibility and thorough evaluation at each stage. The technical and onsite rounds are often spaced a week apart, with prompt feedback following each interview.

Next, let’s dive into the specific interview questions you may encounter throughout the Move ML Engineer process.

3.1. Machine Learning Concepts & Model Design

Expect questions that assess your understanding of ML algorithms, model selection, and how to apply these in real-world scenarios. The focus is on your ability to justify choices and explain trade-offs between speed, accuracy, and complexity.

3.1.1 How would you evaluate and choose between a fast, simple model and a slower, more accurate one for product recommendations?
Discuss how to balance business requirements with model performance, considering factors like interpretability, scalability, and resource constraints. Reference metrics such as precision, recall, and latency, and describe stakeholder communication.

Example answer: "I would start by gathering business requirements—if real-time recommendations are essential, speed may outweigh accuracy. I'd benchmark both models, compare their precision and recall, and present trade-offs to stakeholders, recommending the approach that best aligns with user experience and business goals."

3.1.2 Identify requirements for a machine learning model that predicts subway transit
Outline the data sources, feature engineering, evaluation metrics, and deployment challenges for predictive transit models. Highlight the importance of handling real-time data and scalability.

Example answer: "I'd begin by identifying key features like station location, time of day, and historical ridership. I'd select metrics such as RMSE for prediction accuracy and ensure the model can update in near real-time, factoring in scalability and integration with transit APIs."

3.1.3 Why would one algorithm generate different success rates with the same dataset?
Explain the impact of random initialization, hyperparameters, data splitting, and regularization on model outcomes. Emphasize reproducibility and robust validation.

Example answer: "Variability can stem from random seed initialization, different train-test splits, or hyperparameter choices. To ensure consistent results, I'd standardize my process and use cross-validation to assess performance."

3.1.4 Design a feature store for credit risk ML models and integrate it with SageMaker.
Describe architectural considerations for building a scalable feature store, ensuring data consistency, versioning, and integration with cloud ML platforms.

Example answer: "I'd architect a feature store with robust versioning and metadata tracking, ensuring features are consistently updated and accessible. Integration with SageMaker would involve secure APIs and automated pipelines for feature ingestion and retrieval."

3.1.5 Implement logistic regression from scratch in code
Summarize the key mathematical steps, including the sigmoid function, gradient descent, and loss calculation. Discuss how you would validate and test your implementation.

Example answer: "I'd define the sigmoid activation for probability output, set up gradient descent to optimize weights, and compute binary cross-entropy loss. I'd test the implementation on a small dataset to verify accuracy."

3.2. Data Engineering & System Design

These questions evaluate your ability to design and optimize large-scale data systems, handle ETL processes, and ensure data integrity for downstream ML applications.

3.2.1 Design a data warehouse for a new online retailer
Discuss schema design, partitioning strategies, and ETL pipeline setup. Focus on scalability and supporting real-time analytics.

Example answer: "I'd design a star schema with fact and dimension tables for orders, customers, and inventory. Partitioning by date and product category would improve query speed, and ETL pipelines would ensure timely updates and data integrity."

3.2.2 Write a function to split the data into two lists, one for training and one for testing, without using pandas.
Explain your logic for random sampling and maintaining reproducibility. Highlight how you would handle class imbalance.

Example answer: "I'd shuffle the dataset with a fixed random seed, then slice it into training and testing sets by a specified ratio. For imbalanced data, I'd use stratified sampling to preserve class proportions."

3.2.3 Write a function to return the names and ids for ids that we haven't scraped yet.
Describe how to efficiently compare two lists or sets and optimize for large datasets.

Example answer: "I'd store scraped IDs in a set for fast lookup, then iterate through the master list and collect entries not present in the scraped set, ensuring O(1) lookup time."

3.2.4 Ensuring data quality within a complex ETL setup
Discuss your approach to monitoring, validation, and error handling in ETL pipelines, especially across multiple data sources.

Example answer: "I'd implement automated validation checks for schema consistency and missing values at each ETL stage, log anomalies, and set up alerts for critical errors to ensure downstream reliability."

3.2.5 Modifying a billion rows
Describe strategies for efficient bulk updates, indexing, and minimizing downtime in large-scale databases.

Example answer: "I'd use batch processing, leverage database indexes, and perform updates during off-peak hours. For critical systems, I'd consider online schema changes and incremental updates to avoid downtime."

3.3. Deep Learning & Neural Networks

Here, you’ll encounter questions on neural network architecture, explainability, and scaling models for production. Be ready to articulate concepts clearly for both technical and non-technical audiences.

3.3.1 Explain neural nets to kids
Demonstrate your ability to simplify complex technical concepts using analogies and clear language.

Example answer: "I'd compare neural networks to a group of students learning to recognize animals by looking at pictures and sharing what they notice, gradually getting better with feedback."

3.3.2 Justify a neural network
Explain when a neural network is the right choice, referencing data complexity, non-linearity, and scalability.

Example answer: "I'd recommend a neural network for problems with complex, non-linear relationships or large unstructured datasets, like images or text, where simpler models underperform."

3.3.3 Scaling with more layers
Discuss the challenges and solutions for training deeper networks, including vanishing gradients and regularization.

Example answer: "To scale with more layers, I'd use techniques like batch normalization, skip connections, and careful initialization to mitigate vanishing gradients and overfitting."

3.3.4 Inception architecture
Describe the principles behind inception modules and their advantages for image classification tasks.

Example answer: "Inception architecture uses parallel convolutional layers of different sizes to capture multi-scale features, improving accuracy and computational efficiency in image classification."

3.3.5 Kernel methods
Summarize how kernel methods enable non-linear decision boundaries and their application in support vector machines.

Example answer: "Kernel methods transform data into higher-dimensional spaces, allowing linear algorithms like SVMs to classify non-linear patterns by computing inner products efficiently."

3.4. Data Analysis & Experimentation

Expect to demonstrate your ability to design experiments, analyze results, and communicate findings in business contexts. These questions focus on translating data insights into actionable recommendations.

3.4.1 You work as a data scientist for a ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea. How would you implement it? What metrics would you track?
Describe experiment design, key metrics (conversion, retention, ROI), and how to analyze the causal impact.

Example answer: "I'd run an A/B test to compare riders who receive the discount versus those who don't, tracking metrics like ride volume, revenue per user, and retention. I'd analyze statistical significance and present a recommendation based on ROI."

3.4.2 How do we go about selecting the best 10,000 customers for the pre-launch?
Discuss segmentation strategies, prioritization criteria, and how to balance business goals with fairness.

Example answer: "I'd segment customers by engagement, purchase history, and demographics, then use a scoring system to select the top 10,000 who are likely to provide valuable feedback and maximize launch impact."

3.4.3 How would you analyze how the feature is performing?
Describe the use of key performance indicators, user feedback, and statistical analysis to assess feature success.

Example answer: "I'd track KPIs like conversion rate, engagement, and retention, analyze user feedback, and compare pre- and post-launch metrics to determine the feature's impact."

3.4.4 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior
Explain how you would size a market, design experiments, and interpret results to inform product decisions.

Example answer: "I'd analyze market size using external and internal data, then set up A/B tests to measure user engagement and conversion, using statistical analysis to guide product strategy."

3.4.5 Find the five employees with the highest probability of leaving the company
Outline predictive modeling approaches, feature selection, and how to communicate risk to stakeholders.

Example answer: "I'd build a classification model using features like tenure, performance, and engagement, then rank employees by predicted risk and present actionable insights to HR."

3.5. Communication & Data Accessibility

Questions in this category focus on your ability to present data insights, explain technical concepts to non-experts, and tailor your communication to various audiences.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe strategies for adjusting technical depth and using visuals to maximize impact.

Example answer: "I'd tailor my presentation to the audience's familiarity with data, using clear visuals and analogies, and focusing on actionable takeaways rather than technical jargon."

3.5.2 Making data-driven insights actionable for those without technical expertise
Explain how you bridge the gap between analytics and decision-makers.

Example answer: "I simplify technical findings using relatable examples and visualizations, ensuring stakeholders understand the implications and can confidently act on the insights."

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Highlight your approach to building intuitive dashboards and documentation.

Example answer: "I create dashboards with interactive elements, use plain language in reports, and offer training sessions to empower non-technical users to explore data independently."

3.5.4 How would you answer when an Interviewer asks why you applied to their company?
Connect your career goals and values to the company's mission and culture.

Example answer: "I'm excited by Move's commitment to innovation in mobility and see my ML engineering skills as a perfect fit to drive impactful solutions in this space."

3.5.5 What do you tell an interviewer when they ask you what your strengths and weaknesses are?
Reflect on relevant strengths and areas for growth, showing self-awareness and a willingness to improve.

Example answer: "My strength is building scalable ML pipelines, while I continue to improve my skills in advanced deep learning architectures through ongoing education and hands-on projects."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Describe the context, the analysis performed, and the business impact of your recommendation. Emphasize how your insight led to measurable improvement.

3.6.2 Describe a challenging data project and how you handled it.
Explain the obstacles faced, your problem-solving approach, and how you collaborated or adapted to deliver results.

3.6.3 How do you handle unclear requirements or ambiguity?
Share your strategy for clarifying goals, communicating with stakeholders, and iterating based on feedback.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Discuss how you fostered collaboration and reached consensus, highlighting communication and flexibility.

3.6.5 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Walk through your process for validating data sources, investigating discrepancies, and ensuring data integrity.

3.6.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Outline the automation tools or scripts you built, how you monitored results, and the long-term impact on data reliability.

3.6.7 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Describe your approach to handling missing data, the methods used for imputation or exclusion, and how you communicated uncertainty.

3.6.8 Describe a time you had trouble communicating with stakeholders. How were you able to overcome it?
Share how you adapted your communication style, used visuals or analogies, and ensured alignment on goals.

3.6.9 How do you prioritize multiple deadlines? Additionally, how do you stay organized when you have multiple deadlines?
Explain your prioritization framework, use of tools or processes, and strategies for managing competing demands.

3.6.10 Give an example of learning a new tool or methodology on the fly to meet a project deadline.
Show your resourcefulness, approach to self-learning, and how you applied new knowledge to deliver timely results.

4.1 Company-specific tips:

Familiarize yourself with Move’s core mission of building secure, scalable financial infrastructure. Understand how machine learning directly impacts payment security, fraud detection, and transaction efficiency within fintech. Dive into Move’s recent product launches and APIs, paying close attention to how data-driven automation and compliance are integrated into their offerings.

Research the unique challenges faced by fintech companies in digital payments, such as real-time risk assessment, regulatory constraints, and customer data privacy. Be prepared to discuss how machine learning can be leveraged to solve these specific problems at Move, including examples of anomaly detection, predictive modeling, and operational automation.

Review Move’s approach to open, accessible financial systems. Think about how your experience aligns with their values of transparency, innovation, and reliability. Prepare to articulate how your work as an ML Engineer supports Move’s mission and contributes to safer, more efficient money movement for customers and businesses.

4.2 Role-specific tips:

Demonstrate your ability to build and deploy scalable ML models for financial applications.
Showcase your experience with end-to-end machine learning workflows, from data preprocessing and feature engineering to model selection and deployment. Be ready to discuss how you’ve handled large, complex datasets, particularly those related to transactions, user behavior, and risk signals. Highlight any experience integrating ML models into production environments, especially in cloud platforms such as AWS SageMaker.

Master the trade-offs between model accuracy, latency, and interpretability.
Move’s systems require both speed and reliability, so practice explaining how you balance these priorities. Prepare to discuss scenarios where you’ve chosen between a fast, simple model and a slower, more accurate one, especially in contexts where real-time decision-making is critical. Use metrics like precision, recall, and latency to justify your choices.

Be fluent in Python and SQL, and ready to solve data engineering challenges.
Expect technical questions that test your coding proficiency without relying on external libraries. Practice writing functions for data splitting, ETL pipeline setup, and efficient data manipulation. Emphasize your ability to optimize for performance and reproducibility, especially when working with imbalanced or massive datasets.

Show your expertise in designing robust feature stores and ML infrastructure.
Move values scalable, maintainable systems for feature management and model integration. Prepare to discuss how you would architect a feature store for credit risk models, including strategies for versioning, metadata tracking, and seamless integration with cloud ML platforms. Be specific about how you ensure data consistency and automate feature ingestion.

Articulate your understanding of deep learning and neural networks in business terms.
You may be asked to justify when neural networks are appropriate or explain their architecture to non-technical audiences. Practice simplifying complex concepts using analogies and clear language. Be ready to discuss challenges like vanishing gradients, regularization, and scaling models for production.

Demonstrate strong experiment design and data analysis skills.
Move looks for ML Engineers who can translate business questions into actionable experiments. Prepare to design A/B tests to measure the impact of promotions or product features, define key metrics, and analyze results for statistical significance. Highlight your ability to communicate findings and make data-driven recommendations.

Showcase your communication skills for cross-functional collaboration.
Expect questions that test your ability to present complex insights to both technical and non-technical stakeholders. Practice tailoring your message, using visuals, and focusing on actionable takeaways. Be ready to share examples of bridging the gap between analytics and decision-makers.

Reflect on behavioral scenarios and your approach to ambiguity.
Move values adaptability and teamwork. Prepare stories that demonstrate how you’ve handled unclear requirements, collaborated with colleagues who disagreed, and prioritized multiple deadlines. Show your self-awareness by discussing strengths and areas for growth relevant to ML engineering.

Be ready to discuss data quality and reliability in large-scale systems.
You’ll likely face questions about ensuring data integrity across complex ETL setups and automating data-quality checks. Share your experience building validation pipelines, monitoring for anomalies, and resolving discrepancies between different data sources.

Highlight your resourcefulness and commitment to continuous learning.
Move operates in a fast-paced, evolving environment. Be prepared to give examples of how you’ve quickly learned new tools or methodologies to meet project deadlines, and how you stay up-to-date with advances in machine learning and data engineering.

5.1 How hard is the Move ML Engineer interview?
The Move ML Engineer interview is challenging and designed to rigorously assess both your technical depth and your ability to apply machine learning in real-world fintech scenarios. You’ll be tested on topics ranging from scalable model design and data engineering to system architecture and communication skills. Candidates who thrive are those who can demonstrate hands-on experience with end-to-end ML workflows, solve complex data problems, and clearly articulate their decision-making process. Move values innovative thinkers who can navigate ambiguity and deliver robust solutions for payment infrastructure.

5.2 How many interview rounds does Move have for ML Engineer?
Move’s ML Engineer interview process typically includes five to six rounds: an initial application and resume screen, a recruiter conversation, one or more technical/case interviews, a behavioral interview, and a final onsite or extended virtual round with cross-functional team members. Each stage is designed to evaluate specific competencies, from technical expertise to cultural fit and communication.

5.3 Does Move ask for take-home assignments for ML Engineer?
While take-home assignments are not guaranteed, Move may include a practical case study or coding challenge in the technical interview stage. These assignments assess your ability to solve real ML problems, such as data preprocessing, feature engineering, or designing scalable model pipelines. Expect tasks that mirror the challenges you’ll face on the job, with a focus on clarity, reproducibility, and business impact.

5.4 What skills are required for the Move ML Engineer?
Move seeks ML Engineers with strong proficiency in Python and SQL, deep knowledge of machine learning algorithms, experience with large-scale data engineering, and familiarity with cloud infrastructure (such as AWS SageMaker). You should excel in model selection, feature store design, ETL pipeline development, and experiment analysis. Communication skills are critical—Move values engineers who can present complex insights to both technical and non-technical audiences and collaborate effectively across teams.

5.5 How long does the Move ML Engineer hiring process take?
The typical timeline for the Move ML Engineer interview process is three to four weeks from application to offer. Fast-tracked candidates with highly relevant experience or internal referrals may complete the process in as little as two weeks, while standard timelines allow for scheduling flexibility and comprehensive evaluation at each stage.

5.6 What types of questions are asked in the Move ML Engineer interview?
Expect a mix of technical, analytical, and behavioral questions. Technical rounds cover applied machine learning, coding exercises, data engineering challenges, and system design scenarios. You may be asked to solve case studies, explain model trade-offs, design feature stores, and optimize large-scale data systems. Behavioral interviews assess your teamwork, adaptability, and communication skills, often through scenario-based questions about past projects and decision-making under ambiguity.

5.7 Does Move give feedback after the ML Engineer interview?
Move typically provides feedback through the recruiter after each interview round. While detailed technical feedback may be limited, you can expect high-level insights into your performance and next steps. Move values transparency and strives to keep candidates informed throughout the process.

5.8 What is the acceptance rate for Move ML Engineer applicants?
The Move ML Engineer role is highly competitive, with an estimated acceptance rate below 5%. Move seeks candidates who stand out in both technical expertise and alignment with the company’s mission. Thorough preparation and clear articulation of your impact in previous ML projects will help you distinguish yourself.

5.9 Does Move hire remote ML Engineer positions?
Yes, Move offers remote positions for ML Engineers, with some roles requiring occasional travel for onsite team collaboration or key meetings. Move embraces flexible work arrangements and values engineers who can communicate and deliver results effectively in distributed teams.

• Related guides:
  • Move interview questions
  • Machine Learning Engineer interview guide
  • Top machine learning interview tips

Ready to ace your Move ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Move ML Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Move and similar companies.

With resources like the Move ML Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!