Levelset ML Engineer Interview Guide

Getting ready for an ML Engineer interview at Levelset? The Levelset ML Engineer interview process typically spans multiple question topics and evaluates skills in areas like machine learning system design, data preparation and cleaning, model evaluation, and communicating technical concepts to non-technical audiences. Interview preparation is especially important for this role, as ML Engineers at Levelset are expected to build scalable solutions for real-world business challenges, collaborate across teams, and translate complex data into actionable insights that drive process improvements.

In preparing for the interview, you should:

• Understand the core skills necessary for ML Engineer positions at Levelset.
• Gain insights into Levelset’s ML Engineer interview structure and process.
• Practice real Levelset ML 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 Levelset ML Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

Levelset is a leading technology company in the construction industry, providing software solutions that streamline payment processes and improve cash flow management for contractors, suppliers, and property owners. By digitizing lien rights and documentation workflows, Levelset helps users reduce payment delays and minimize financial risk on construction projects. The company’s mission is to simplify complex payment systems and foster transparency across the construction ecosystem. As an ML Engineer, you will contribute to building intelligent features that automate document analysis and enhance the overall efficiency of payment operations.

As an ML Engineer at Levelset, you are responsible for designing, building, and deploying machine learning models that enhance the company’s construction payment solutions. You will work closely with data scientists, software engineers, and product teams to develop algorithms that automate document processing, risk assessment, and data extraction from complex construction documents. Your core tasks include preprocessing large datasets, selecting and training appropriate models, and integrating ML solutions into Levelset’s products. This role plays a key part in improving operational efficiency, reducing payment risks, and supporting Levelset’s mission to simplify payments in the construction industry.

2.1 Stage 1: Application & Resume Review

The process begins with a comprehensive screening of your application and resume. The recruiting team looks for evidence of robust machine learning engineering experience, including expertise in designing and deploying ML models, proficiency with Python, SQL, and data manipulation, and experience working with large, complex datasets. Demonstrated success in building scalable ML systems, addressing data quality issues, and collaborating cross-functionally is highly valued. To prepare, ensure your resume highlights relevant ML projects, quantifiable achievements, and your technical toolkit.

2.2 Stage 2: Recruiter Screen

Next, you’ll typically have a phone or video call with a recruiter. This conversation is focused on your background, motivations for joining Levelset, and alignment with the company’s mission and values. The recruiter may touch on your experience with ML tools, data engineering, and communication skills, as well as your familiarity with collaborative environments. Prepare by articulating your career trajectory, key accomplishments, and why Levelset’s domain interests you.

2.3 Stage 3: Technical/Case/Skills Round

This stage is often conducted by a senior ML engineer or data team lead and may include one or more rounds. You’ll be assessed on your ability to solve ML problems, design scalable systems, and demonstrate proficiency in Python, SQL, and data wrangling. Expect to tackle case studies—such as designing ML models for real-world applications (e.g., content moderation, financial insights extraction, or transit prediction), addressing imbalanced data, or building ETL pipelines. You may also encounter algorithmic coding exercises and questions on neural networks, kernel methods, decision trees, and feature engineering. Preparation should involve reviewing key ML concepts, system design patterns, and coding best practices.

2.4 Stage 4: Behavioral Interview

Behavioral interviews are typically led by hiring managers or cross-functional partners. You’ll be asked to reflect on your teamwork, adaptability, and problem-solving approach in ambiguous or challenging situations. Expect to discuss past projects, hurdles faced in data initiatives, and how you communicate complex technical ideas to non-technical stakeholders. Prepare by thinking through examples where you demonstrated leadership, resolved conflicts, or made data accessible and actionable.

2.5 Stage 5: Final/Onsite Round

The final round often consists of multiple interviews with technical leads, product managers, and potentially executives. You’ll be evaluated on advanced ML engineering skills, system design, and your ability to collaborate across teams. Sessions may include deep dives into previous projects, whiteboarding system architectures (such as scalable ETL pipelines or digital classroom services), and live problem-solving. Cultural fit, strategic thinking, and communication are also assessed at this stage. Preparation should focus on articulating your design choices, tradeoffs, and impact in prior roles.

2.6 Stage 6: Offer & Negotiation

If you successfully navigate the interview rounds, the recruiter will present an offer. This stage involves discussion of compensation, benefits, and start date, as well as clarifying any remaining questions about the role or team structure. Be ready to negotiate based on your experience and the value you bring to Levelset’s ML initiatives.

2.7 Average Timeline

The Levelset ML Engineer interview process typically spans 3-5 weeks from application to offer. Candidates with highly relevant experience or strong internal referrals may progress faster, completing the process in as little as 2-3 weeks. Standard pacing allows about a week between each stage, with technical rounds and onsite interviews scheduled based on participant availability.

Now, let’s review the types of interview questions you can expect at each stage.

3.1. Machine Learning System Design

Expect to be assessed on your ability to design real-world ML systems from problem framing through to deployment. Focus on how you handle requirements gathering, model selection, evaluation, and scalability. Demonstrate your understanding of trade-offs and practical considerations in production environments.

3.1.1 Identify requirements for a machine learning model that predicts subway transit
Start by clarifying the prediction target, necessary features, and available data sources. Discuss how you would handle data collection, preprocessing, and model validation while considering operational constraints.

3.1.2 Designing an ML system for unsafe content detection
Describe your approach to building a scalable and robust ML pipeline for content moderation, including data labeling, model selection, and continuous improvement with feedback loops.

3.1.3 Designing an ML system to extract financial insights from market data for improved bank decision-making
Explain how you would architect a solution that ingests external data, processes it via APIs, and transforms it into actionable insights for downstream business units.

3.1.4 System design for a digital classroom service
Outline your system architecture, focusing on user data flows, personalization, and the integration of predictive models to enhance the learning experience.

3.2. Machine Learning Algorithms & Evaluation

You’ll be tested on your grasp of core ML concepts, algorithm selection, and evaluation metrics. Emphasize your ability to justify choices and interpret model performance in a business context.

3.2.1 Addressing imbalanced data in machine learning through carefully prepared techniques
Discuss strategies like resampling, class weighting, and appropriate evaluation metrics, and explain how you would ensure model robustness on minority classes.

3.2.2 Creating a machine learning model for evaluating a patient's health
Detail your end-to-end process, including feature engineering, model selection, and how you would validate the model’s medical relevance and accuracy.

3.2.3 Write a query to compute the average time it takes for each user to respond to the previous system message
Explain how you’d use time-based features and windowing functions to create predictive features or evaluate response time as a model target.

3.2.4 Write a Python function to divide high and low spending customers
Describe your approach to threshold selection, potentially using clustering or statistical analysis, and how you’d validate the business impact of your segments.

3.3. Deep Learning & Model Explainability

For ML Engineer roles, you’ll need to show a practical understanding of deep learning architectures, their limitations, and how to communicate complex concepts to diverse audiences.

3.3.1 Explain neural nets to kids
Use simple analogies to break down the structure and function of neural networks, focusing on intuition rather than technical jargon.

3.3.2 Scaling with more layers
Discuss the benefits and challenges of deepening neural networks, including vanishing gradients, overfitting, and computational costs.

3.3.3 Kernel methods
Summarize how kernel methods enable non-linear modeling, and when you’d prefer them over deep learning or tree-based methods.

3.3.4 Justify a neural network
Explain how you decide when a neural network is the right tool for a problem, considering data size, complexity, and interpretability needs.

3.4. Data Engineering & Pipeline Design

ML Engineers are expected to have strong data engineering skills. You’ll be asked about building scalable pipelines, cleaning messy datasets, and ensuring data quality.

3.4.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners
Describe how you’d build a robust, modular ETL pipeline that handles schema variations and data quality checks.

3.4.2 Describing a real-world data cleaning and organization project
Walk through your process for profiling, cleaning, and validating data, including any automation or quality assurance steps.

3.4.3 Ensuring data quality within a complex ETL setup
Explain the methods you use to monitor, test, and remediate data quality issues, especially in multi-source environments.

3.4.4 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Discuss how you would standardize and validate data from inconsistent sources, and the impact of proper formatting on downstream ML tasks.

3.5. Experimental Design & Metrics

Demonstrate your ability to design, implement, and interpret experiments, as well as select and track the right metrics for business impact.

3.5.1 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you’d set up, run, and interpret an A/B test, including hypothesis formulation and statistical significance.

3.5.2 You work as a data scientist for 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?
Outline your experiment design, key performance indicators, and how you’d analyze both short-term and long-term effects.

3.5.3 How would you design user segments for a SaaS trial nurture campaign and decide how many to create?
Explain your segmentation approach, including data analysis, clustering, and validation of segment effectiveness.

3.5.4 Experimental rewards system and ways to improve it
Discuss how you’d test and iterate on a rewards system, including metric selection and feedback loops.

3.6. Behavioral Questions

3.6.1 Describe a challenging data project and how you handled it.
Share a specific example where you navigated technical, stakeholder, or resource challenges; focus on your problem-solving process and the outcome.

3.6.2 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying objectives, communicating with stakeholders, and iterating quickly to reduce uncertainty.

3.6.3 Tell me about a time you delivered critical insights even though a significant portion of the dataset had nulls. What analytical trade-offs did you make?
Describe your method for handling missing data, how you assessed the impact on analysis, and how you communicated uncertainty.

3.6.4 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Focus on how you bridged gaps in understanding using tangible prototypes, and how that led to consensus or improved project direction.

3.6.5 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Highlight your communication and persuasion skills, and how you used evidence to build trust and drive action.

3.6.6 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Discuss your approach to data validation, cross-referencing, and stakeholder alignment to resolve data discrepancies.

3.6.7 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Explain the automation or tooling you implemented, and the impact it had on data reliability and team efficiency.

3.6.8 Tell me about a time you used sampling or sketching techniques to profile a huge raw table that wouldn’t fit in memory.
Describe your technical approach and how you ensured your sample was representative for analysis.

3.6.9 How did you communicate uncertainty to executives when your cleaned dataset covered only part of the total transactions?
Focus on transparency, use of confidence intervals, and how you maintained trust while conveying limitations.

3.6.10 Describe a time you had to deliver an overnight report and still guarantee the numbers were “executive reliable.” How did you balance speed with data accuracy?
Share your triage approach, prioritizing critical checks and communicating any caveats or limitations to leadership.

4.1 Company-specific tips:

Familiarize yourself with Levelset’s mission to simplify payment processes and increase transparency in the construction industry. Understand how the company’s platform digitizes lien rights, automates document workflows, and mitigates payment delays for contractors and suppliers. Research recent product launches, partnerships, or case studies to grasp how Levelset leverages technology to solve real business challenges.

Take time to explore how machine learning can be applied to automate document analysis, risk assessment, and financial insights extraction within the construction payments ecosystem. Consider the types of unstructured data Levelset handles—such as contracts, lien waivers, and invoices—and think about how ML models could improve data extraction, validation, and business decision-making.

Demonstrate your ability to communicate complex ML concepts to non-technical audiences, as Levelset values clear, actionable insights that drive operational efficiency. Prepare to speak about how your work can directly impact payment speed, risk reduction, and customer experience in a construction context.

4.2 Role-specific tips:

4.2.1 Practice designing robust ML systems for document automation and risk prediction.
Focus on building end-to-end machine learning solutions that automate document classification, extract key information from contracts, and assess payment risk. Be ready to discuss how you would gather requirements, select appropriate models, and deploy scalable systems that handle large volumes of construction documents with varying formats and quality.

4.2.2 Strengthen your data engineering skills—especially in cleaning and organizing messy, heterogeneous datasets.
Levelset deals with complex and inconsistent data sources, so practice profiling, cleaning, and validating data from multiple origins. Be prepared to share examples of how you standardized formats, handled missing values, and automated data quality checks to ensure reliable downstream analysis.

4.2.3 Review techniques for addressing imbalanced data and selecting appropriate evaluation metrics.
Construction data often includes rare events or minority classes, such as payment disputes or high-risk transactions. Brush up on resampling methods, class weighting, and metrics like precision, recall, and F1-score. Be ready to explain how your choices ensure robust model performance, especially on critical business outcomes.

4.2.4 Prepare to justify your model choices and communicate technical concepts simply.
You may need to explain neural networks, kernel methods, or decision trees to stakeholders with limited ML background. Practice using analogies and clear language to articulate why you selected a particular algorithm, what trade-offs you considered, and how the model’s predictions translate into business value.

4.2.5 Demonstrate your ability to design and optimize scalable ETL pipelines.
Showcase your experience building modular data pipelines that ingest, transform, and validate data from diverse sources. Be ready to discuss how you ensure data integrity, monitor for quality issues, and adapt pipelines to new business requirements or changing data schemas.

4.2.6 Exhibit strong experimental design skills, especially around A/B testing and metrics selection.
Be prepared to outline how you would set up experiments to measure the impact of new ML features or process changes. Focus on hypothesis formulation, control/treatment group design, and tracking metrics that align with Levelset’s business goals, such as payment speed or risk reduction.

4.2.7 Bring examples of cross-functional collaboration and stakeholder communication.
Levelset values teamwork and the ability to align diverse groups around data-driven solutions. Share stories where you bridged gaps between technical and non-technical teams, used prototypes to clarify requirements, or influenced decision-making without formal authority.

4.2.8 Practice quick, reliable analytics and communicating uncertainty.
You may be asked to deliver insights under tight deadlines or with incomplete data. Prepare to discuss your approach to balancing speed and accuracy, automating critical checks, and transparently communicating data limitations or confidence intervals to leadership.

4.2.9 Be ready to discuss your approach to resolving data discrepancies and automating data-quality checks.
Have examples on hand of how you validated metrics across multiple source systems, built trust with stakeholders, and implemented automation to prevent recurring data issues. Highlight your commitment to data reliability and continuous improvement.

4.2.10 Show your ability to profile and analyze large datasets that don’t fit in memory.
Practice using sampling, sketching, or windowing techniques to efficiently explore and summarize massive tables. Be prepared to explain how you ensured your sample was representative and how you derived actionable insights from partial data.

5.1 How hard is the Levelset ML Engineer interview?
The Levelset ML Engineer interview is challenging, especially for candidates new to building production-grade machine learning systems. You’ll be tested on end-to-end ML design, data engineering, model evaluation, and your ability to communicate complex concepts to diverse stakeholders. The interview rewards those who can demonstrate real-world impact, scalability, and cross-functional collaboration in ML projects.

5.2 How many interview rounds does Levelset have for ML Engineer?
Levelset typically conducts 5-6 interview rounds for ML Engineer candidates. The process includes an application and resume review, a recruiter screen, technical/case rounds, a behavioral interview, a final onsite or virtual round with technical and product leads, followed by offer and negotiation.

5.3 Does Levelset ask for take-home assignments for ML Engineer?
Levelset may include a take-home assignment or case study in the technical interview stage. These assignments often focus on designing scalable ML solutions, cleaning and preparing complex datasets, or developing algorithms for document automation or risk assessment relevant to the construction payments domain.

5.4 What skills are required for the Levelset ML Engineer?
Key skills include proficiency in Python and SQL, hands-on experience with machine learning model design and deployment, data cleaning and engineering, scalable ETL pipeline development, and strong communication abilities. Familiarity with deep learning, handling imbalanced data, experimental design, and business-oriented metrics is highly valued.

5.5 How long does the Levelset ML Engineer hiring process take?
The Levelset ML Engineer hiring process usually takes 3-5 weeks from initial application to offer. Timelines may vary based on candidate scheduling, interviewer availability, and the complexity of technical assessments.

5.6 What types of questions are asked in the Levelset ML Engineer interview?
Expect a blend of machine learning system design, algorithm selection, coding exercises, data engineering challenges, and behavioral questions. You’ll encounter real-world case studies such as automating document processing, designing ETL pipelines, handling messy data, and communicating model results to non-technical teams.

5.7 Does Levelset give feedback after the ML Engineer interview?
Levelset generally provides feedback through recruiters after interview rounds. The feedback is often high-level, focusing on strengths and areas for improvement, though detailed technical feedback may be limited.

5.8 What is the acceptance rate for Levelset ML Engineer applicants?
While Levelset does not publish specific acceptance rates, the ML Engineer role is competitive, with an estimated acceptance rate of 3-7% for qualified candidates. Strong technical skills and relevant industry experience can significantly improve your chances.

5.9 Does Levelset hire remote ML Engineer positions?
Yes, Levelset offers remote opportunities for ML Engineers. Some positions may require occasional travel for team meetings or onsite collaboration, but remote work is supported for most technical roles.

• Related guides:
  • Levelset interview questions
  • ML Engineer interview guide
  • Top machine learning interview tips

Ready to Ace Your Interview?

Ready to ace your Levelset ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a Levelset 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 Levelset and similar companies.

With resources like the Levelset 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!