Included Health ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Included Health? The Included Health Machine Learning Engineer interview process typically spans technical, business, and behavioral question topics, and evaluates skills in areas like designing and deploying ML models, data pipeline engineering, healthcare analytics, and communicating insights to diverse stakeholders. Interview preparation is especially important for this role at Included Health, as candidates are expected to demonstrate expertise in building robust machine learning solutions that directly impact patient outcomes, operational efficiency, and product innovation within a highly regulated and data-driven healthcare environment.

In preparing for the interview, you should:

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

Included Health is a leading digital health company that provides comprehensive, personalized healthcare navigation and virtual care services for employers and health plans. The company leverages technology, including machine learning, to guide members through the complex healthcare system, offering expert medical opinions, care coordination, and access to high-quality virtual and in-person care. With a mission to raise the standard of healthcare for everyone, Included Health serves millions of members across the U.S. As an ML Engineer, you will help develop and implement data-driven solutions that enhance care experiences and outcomes for diverse patient populations.

As an ML Engineer at Included Health, you will design, develop, and deploy machine learning models that enhance the company’s healthcare platform. Your responsibilities include collaborating with data scientists, software engineers, and product teams to build scalable solutions that improve patient outcomes and streamline healthcare processes. You will work with large and complex healthcare datasets, implement robust data pipelines, and optimize algorithms for real-world performance. This role is key in driving innovation and supporting Included Health’s mission to provide personalized and accessible healthcare through advanced technology.

2.1 Stage 1: Application & Resume Review

At Included Health, the ML Engineer interview process begins with a thorough review of your application and resume by the recruiting team. They look for strong foundational experience in machine learning, proficiency in Python, and hands-on exposure to model development, deployment, and data pipeline design. Highlight your expertise in building end-to-end ML solutions, working with healthcare data, and implementing scalable systems. Ensure your resume demonstrates impact through projects involving risk assessment models, data cleaning, and health metrics analysis.

2.2 Stage 2: Recruiter Screen

The recruiter screen is typically a 30-minute call with a technical recruiter or talent acquisition partner. This stage assesses your interest in Included Health, overall fit with the company’s values, and your motivation for pursuing the ML Engineer role. Expect to discuss your background, relevant healthcare or data science experience, and your understanding of the company’s mission. Prepare to articulate why you want to work at Included Health and how your skills align with their focus on improving patient outcomes and community health.

2.3 Stage 3: Technical/Case/Skills Round

This round is usually conducted virtually and led by a senior ML engineer or a member of the data science team. You’ll be evaluated on your technical depth in machine learning, coding ability in Python, and approach to solving real-world healthcare problems. The session may include live coding exercises, system design questions (e.g., risk assessment models, data pipelines for health metrics), and case studies on handling imbalanced data or building secure ML systems. Prepare to discuss your experience with distributed systems, feature engineering, and integrating ML models with APIs for downstream tasks.

2.4 Stage 4: Behavioral Interview

The behavioral interview is often conducted by a cross-functional team member or hiring manager. This stage explores your collaboration skills, adaptability, and ability to communicate complex insights to non-technical stakeholders. You’ll be asked about past experiences overcoming hurdles in data projects, presenting findings to diverse audiences, and working within ethical guidelines for sensitive healthcare data. Prepare to share stories that highlight your strengths, weaknesses, and commitment to teamwork and patient privacy.

2.5 Stage 5: Final/Onsite Round

The final round typically consists of multiple interviews (virtual or onsite) with engineering leadership, product managers, and potential team members. This stage dives deeper into your technical expertise, product sense, and strategic thinking. You may be asked to design end-to-end ML solutions for healthcare scenarios, evaluate business impact of ML models, and discuss system architecture for large-scale patient data. Demonstrate your ability to balance technical rigor with user-centric design and ethical considerations.

2.6 Stage 6: Offer & Negotiation

Once you successfully navigate the previous rounds, you’ll engage with the recruiting team for offer presentation and negotiation. This step involves discussing compensation, benefits, equity, and your potential impact within the ML engineering team. Be prepared to clarify any remaining questions about the role, team culture, and career growth opportunities at Included Health.

2.7 Average Timeline

The typical Included Health ML Engineer interview process spans 3-5 weeks from initial application to offer. Fast-track candidates with highly relevant healthcare ML experience or strong referrals may move through the process in as little as 2-3 weeks, while the standard pace allows for about one week between each stage. Scheduling for technical and onsite rounds may vary based on team availability.

Next, let’s break down the types of interview questions you can expect at each stage.

3.1. Machine Learning System Design

Expect questions that assess your ability to design, evaluate, and deploy machine learning systems in real-world healthcare and operational contexts. Focus on explaining your approach to problem formulation, data selection, model architecture, and how you ensure reliability and scalability.

3.1.1 Creating a machine learning model for evaluating a patient's health
Describe your process for building a health risk assessment model, covering data sources, feature engineering, model selection, and validation. Emphasize how you account for biases and ensure clinical utility.

3.1.2 Designing an ML system for unsafe content detection
Explain how you would architect a scalable pipeline for detecting unsafe content, including data labeling, model training, deployment, and ongoing monitoring for false positives/negatives.

3.1.3 Building a model to predict if a driver on Uber will accept a ride request or not
Walk through the end-to-end process: data gathering, feature engineering, model choice, and evaluation. Discuss how you'd handle class imbalance and model interpretability.

3.1.4 Identify requirements for a machine learning model that predicts subway transit
Outline the factors and data required to accurately predict subway transit, considering external variables and real-time updates. Discuss how you'd validate and monitor the model in production.

3.1.5 Designing a secure and user-friendly facial recognition system for employee management while prioritizing privacy and ethical considerations
Describe your approach to balancing usability, security, and privacy in a facial recognition system. Highlight your understanding of regulatory and ethical frameworks.

3.2. Data Engineering & Pipelines

These questions test your ability to design robust, scalable data pipelines and manage large-scale data processing. Focus on your experience with ETL, data validation, and ensuring data quality for downstream ML applications.

3.2.1 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Explain how you’d structure the pipeline, from ingestion and cleaning to feature engineering and serving, ensuring data freshness and reliability.

3.2.2 Write a function that splits the data into two lists, one for training and one for testing.
Describe your approach for splitting datasets, ensuring reproducibility and avoiding data leakage.

3.2.3 Write a query to find all dates where the hospital released more patients than the day prior
Discuss how you’d use window functions or self-joins to compare sequential days and extract the required insights efficiently.

3.2.4 Calculate the 3-day rolling average of steps for each user.
Explain how you’d implement rolling calculations in SQL or Python, handling edge cases and missing data.

3.3. Model Evaluation & Statistical Reasoning

These questions assess your understanding of experimental design, statistical testing, and model evaluation—crucial for ML engineers working on healthcare or operational models. Be ready to articulate the metrics and validation strategies you use.

3.3.1 How would you evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Discuss experimental design (e.g., A/B testing), key metrics (conversion, retention, profit), and how you’d interpret the results.

3.3.2 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you’d structure an A/B test, select KPIs, and ensure statistical validity in your conclusions.

3.3.3 Addressing imbalanced data in machine learning through carefully prepared techniques.
Explain strategies for handling imbalanced data, such as resampling, class weighting, and evaluation metrics that account for imbalance.

3.3.4 Implement logistic regression from scratch in code
Outline the mathematical approach and logic behind implementing logistic regression, focusing on gradient descent and model convergence.

3.4. Communication & Stakeholder Management

ML engineers must communicate complex ideas clearly and adapt their presentation to different audiences. These questions evaluate your ability to translate technical insights into actionable recommendations.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Share your framework for tailoring presentations, using visuals, and simplifying technical jargon for non-technical stakeholders.

3.4.2 Describing a real-world data cleaning and organization project
Walk through a project where you cleaned and structured messy data, highlighting your systematic approach and communication with stakeholders about data quality.

3.4.3 Describing a data project and its challenges
Discuss how you identified and overcame obstacles in a data project, focusing on problem-solving and collaboration.

3.4.4 What do you tell an interviewer when they ask you what your strengths and weaknesses are?
Be honest and reflective, choosing strengths relevant to ML engineering and weaknesses you’re actively addressing.

3.5. Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe a situation where your analysis directly led to a business or product change. Highlight your end-to-end process from data exploration to recommendation.

3.5.2 Describe a challenging data project and how you handled it.
Share the context, the technical or organizational hurdles, and the steps you took to overcome them. Emphasize your problem-solving and persistence.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your approach for clarifying objectives, asking targeted questions, and iterating with stakeholders to define success.

3.5.4 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?
Describe how you facilitated open discussion, considered alternative viewpoints, and built consensus or adapted your solution as needed.

3.5.5 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Share how you led cross-functional alignment, defined clear metrics, and documented the agreed-upon definitions.

3.5.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Explain your persuasion strategy, how you built trust, and the outcome of your efforts.

3.5.7 Describe a time you had to deliver an overnight churn report and still guarantee the numbers were “executive reliable.” How did you balance speed with data accuracy?
Discuss your triage, prioritization, and communication strategies to maintain quality under tight deadlines.

3.5.8 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Explain the trade-offs you made and how you ensured that future improvements were planned.

3.5.9 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
Highlight your accountability, how you communicated the issue, and the steps you took to correct it and prevent recurrence.

3.5.10 Give an example of learning a new tool or methodology on the fly to meet a project deadline.
Describe the situation, how you approached the learning curve, and the impact on project delivery.

4.1 Company-specific tips:

Familiarize yourself with Included Health’s mission to improve healthcare navigation and virtual care through technology. Demonstrate your understanding of how machine learning can enhance patient outcomes, streamline care coordination, and support high-quality medical opinions within a regulated environment.

Research how Included Health leverages data-driven solutions, especially in areas like risk assessment, health metrics analysis, and personalized care recommendations. Be prepared to discuss recent trends in digital health, such as telemedicine, interoperability, and patient privacy, and how ML can drive innovation in these domains.

Understand the unique challenges of working with healthcare data, including compliance with HIPAA, data anonymization, and ethical considerations. Be ready to articulate your experience handling sensitive data and ensuring that ML models are both secure and fair.

4.2 Role-specific tips:

4.2.1 Practice designing ML models for healthcare scenarios, such as risk assessment and patient outcome prediction.
Prepare to walk through your approach to building models that evaluate patient health, including data selection, feature engineering, and validation strategies. Highlight how you address data biases and ensure clinical relevance.

4.2.2 Demonstrate proficiency in building scalable data pipelines for large and complex healthcare datasets.
Showcase your experience designing robust ETL processes, ensuring data quality, and integrating data from multiple sources. Be ready to discuss how you maintain data freshness and reliability for downstream ML applications.

4.2.3 Prepare to discuss strategies for handling imbalanced data and evaluating model performance in healthcare contexts.
Explain techniques such as resampling, class weighting, and choosing appropriate evaluation metrics like precision, recall, and ROC-AUC. Illustrate your understanding of why these approaches are critical in healthcare applications.

4.2.4 Highlight your ability to communicate complex ML insights to cross-functional stakeholders, including clinicians and product managers.
Practice simplifying technical concepts and tailoring presentations to different audiences. Use examples where you translated data findings into actionable business or clinical recommendations.

4.2.5 Be ready to address privacy, security, and ethical considerations in ML model development and deployment.
Discuss how you build secure systems, anonymize sensitive data, and comply with healthcare regulations. Share examples of balancing usability and privacy in your solutions.

4.2.6 Prepare stories that showcase your collaboration and adaptability in multidisciplinary teams.
Reflect on past experiences where you worked closely with engineers, data scientists, and healthcare professionals to solve complex problems. Emphasize your strengths in teamwork and communication.

4.2.7 Practice coding and system design questions in Python, focusing on ML algorithms, data manipulation, and API integration.
Brush up on implementing models from scratch, designing end-to-end ML workflows, and integrating your solutions with production systems. Be ready to explain your code and design decisions clearly.

4.2.8 Articulate your approach to experimental design, statistical reasoning, and interpreting results in real-world healthcare projects.
Discuss how you conduct A/B tests, select key metrics, and ensure statistical validity. Illustrate your impact by sharing examples where your analysis led to product or operational improvements.

4.2.9 Prepare to share examples of overcoming challenges in data projects, such as unclear requirements, conflicting definitions, or tight deadlines.
Think of specific situations where you clarified objectives, aligned teams, and balanced speed with data integrity. Highlight your problem-solving and leadership skills.

4.2.10 Be honest and thoughtful when discussing your strengths and weaknesses as an ML Engineer.
Choose strengths that align with Included Health’s mission, such as technical rigor, empathy, and accountability. For weaknesses, focus on areas you’re actively improving and how you seek feedback to grow.

5.1 How hard is the Included Health ML Engineer interview?
The Included Health ML Engineer interview is considered challenging due to its focus on both technical depth and domain-specific knowledge in healthcare. You’ll be expected to demonstrate advanced machine learning skills, experience with large-scale data pipelines, and an understanding of healthcare data privacy and regulatory requirements. The process is thorough, assessing your ability to build robust ML solutions that impact patient care and operational efficiency.

5.2 How many interview rounds does Included Health have for ML Engineer?
Typically, the interview process consists of 5-6 rounds: an initial recruiter screen, technical/case interviews, behavioral interviews, and a final onsite round with engineering leadership and cross-functional stakeholders. Each stage is designed to evaluate different aspects of your expertise, from coding and system design to communication and collaboration.

5.3 Does Included Health ask for take-home assignments for ML Engineer?
Included Health occasionally provides take-home assignments, especially for technical roles like ML Engineer. These assignments often involve designing or coding a machine learning solution relevant to healthcare, such as building a risk assessment model or structuring a data pipeline. The goal is to assess your practical skills, problem-solving approach, and ability to communicate your methodology.

5.4 What skills are required for the Included Health ML Engineer?
Key skills include proficiency in Python, experience designing and deploying ML models, building scalable data pipelines, and working with complex healthcare datasets. You’ll also need strong statistical reasoning, knowledge of model evaluation techniques, and the ability to communicate technical insights to non-technical stakeholders. Familiarity with healthcare regulations, privacy, and ethical considerations is highly valued.

5.5 How long does the Included Health ML Engineer hiring process take?
The typical timeline is 3-5 weeks from initial application to offer. Fast-track candidates with direct healthcare ML experience or referrals may move through in 2-3 weeks, while the standard pace allows for about a week between each interview stage. Scheduling may vary depending on team availability and candidate logistics.

5.6 What types of questions are asked in the Included Health ML Engineer interview?
You’ll encounter a mix of technical and behavioral questions. Expect system design problems, live coding exercises, case studies on healthcare analytics, and questions about building secure ML solutions. You’ll also be asked about your approach to data engineering, handling imbalanced datasets, experimental design, and communicating insights to diverse teams. Behavioral questions will explore your collaboration, adaptability, and ethical decision-making.

5.7 Does Included Health give feedback after the ML Engineer interview?
Included Health generally provides feedback through the recruiting team, especially if you reach the onsite or final interview stages. While detailed technical feedback may be limited, you can expect high-level insights about your strengths and areas for improvement. The company values transparency and aims to help candidates grow from the experience.

5.8 What is the acceptance rate for Included Health ML Engineer applicants?
While specific acceptance rates aren’t publicly available, the ML Engineer role at Included Health is highly competitive. It’s estimated that only a small percentage of applicants receive offers, reflecting the rigorous selection process and the company’s high standards for technical and domain expertise.

5.9 Does Included Health hire remote ML Engineer positions?
Yes, Included Health offers remote opportunities for ML Engineers, with some roles requiring occasional in-person collaboration or office visits. The company embraces flexible work arrangements, especially for technical talent, and supports remote onboarding and integration into distributed teams.

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

With resources like the Included Health ML Engineer Interview Guide, Machine Learning Engineer interview guide, and our latest healthcare data science and ML projects, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

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