Globus Medical ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at Globus Medical? The Globus Medical ML Engineer interview process typically spans 4–6 question topics and evaluates skills in areas like machine learning model development, data preparation and cleaning, algorithmic reasoning, and communicating complex technical concepts. Interview preparation is especially important for this role at Globus Medical, as candidates are expected to design and implement robust ML solutions tailored for healthcare applications, address challenges such as imbalanced data and risk assessment, and clearly articulate their approach to both technical and non-technical audiences.

In preparing for the interview, you should:

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

Globus Medical is a leading medical device company specializing in the design, development, and commercialization of innovative solutions for musculoskeletal disorders. Operating at the intersection of healthcare and advanced technology, Globus Medical offers a broad portfolio that includes spinal, orthopedic, and robotic surgical products. The company is committed to improving patient care through cutting-edge research and engineering. As an ML Engineer, you will contribute to developing intelligent healthcare solutions that enhance surgical outcomes and streamline clinical workflows, directly supporting Globus Medical’s mission to advance patient care through technological innovation.

As an ML Engineer at Globus Medical, you will design, develop, and deploy machine learning models to support innovative medical technologies and solutions. Your responsibilities include collaborating with cross-functional teams such as software engineers, data scientists, and clinical experts to build data-driven tools that enhance patient care and surgical outcomes. You will work on tasks like data preprocessing, algorithm selection, model training, and performance evaluation, ensuring models meet rigorous healthcare standards. This role is integral to advancing Globus Medical’s mission of improving patient outcomes through technological innovation in the medical device industry.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough screening of your resume and application materials by the recruiting team, focusing on your experience with machine learning model development, data preparation, and deployment within healthcare or regulated industries. Emphasis is placed on technical proficiency in Python, SQL, and ML frameworks, as well as evidence of impactful projects related to risk assessment, predictive modeling, and large-scale data analysis. To prepare, ensure your resume clearly highlights relevant ML engineering experience, successful project outcomes, and any exposure to medical or device data.

2.2 Stage 2: Recruiter Screen

Next, a recruiter will reach out for a 30-minute phone call to discuss your background, motivation for joining Globus Medical, and alignment with the ML Engineer role. Expect questions about your technical skills, familiarity with healthcare data, and communication abilities. Preparation should include concise examples of your work with ML systems, effective data visualization, and your approach to demystifying complex concepts for non-technical stakeholders.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one to two rounds conducted virtually by current ML engineers or data science leads. You will be assessed through live coding exercises, algorithmic problem-solving, and case studies relevant to healthcare. Topics may include building risk assessment models, handling imbalanced datasets, designing streaming data pipelines, and explaining neural networks or kernel methods. Preparation should focus on demonstrating hands-on coding skills, model evaluation strategies, and clear technical communication.

2.4 Stage 4: Behavioral Interview

A behavioral interview, often with the hiring manager or cross-functional team members, will probe your collaboration style, adaptability, and ability to navigate challenges in data projects. You'll be expected to discuss your experiences presenting complex insights, overcoming hurdles in ML deployments, and working with diverse teams. Prepare by reflecting on past situations where you demonstrated leadership, resilience, and stakeholder management in data-driven environments.

2.5 Stage 5: Final/Onsite Round

The final stage may be onsite or virtual, typically comprising multiple interviews with senior engineers, product managers, and sometimes clinical experts. You’ll be challenged on end-to-end ML system design, ethical considerations in healthcare data, and your approach to communicating findings to both technical and non-technical audiences. Preparation should include reviewing advanced ML concepts (e.g., transformer architectures, SVMs vs. deep learning), real-world case studies, and strategies for making data accessible.

2.6 Stage 6: Offer & Negotiation

Once interviews are completed, the recruiter will present an offer and facilitate negotiation around compensation, role expectations, and start date. This stage may include a brief discussion with the hiring manager to clarify any final details or team fit considerations.

2.7 Average Timeline

The typical Globus Medical ML Engineer interview process spans 3–5 weeks from application to offer, with fast-track candidates occasionally completing all rounds in as little as 2 weeks. Most candidates experience about a week between each stage, and scheduling for technical and final rounds depends on team availability. Take-home assignments or case studies may have a 3–5 day turnaround, while onsite rounds are usually consolidated into a single day.

Next, let’s explore the types of interview questions you can expect throughout each stage.

3.1 Machine Learning System Design

Machine learning system design questions at Globus Medical often focus on your ability to architect robust, scalable, and practical solutions for real-world health and technology problems. You'll be asked to discuss trade-offs, data requirements, and how your models would be deployed in production environments. Be ready to justify your choices and discuss how you would monitor and iterate on your models post-deployment.

3.1.1 Creating a machine learning model for evaluating a patient's health
Describe your process for building a predictive health model, including data sourcing, feature engineering, model selection, and validation. Emphasize how you would ensure model interpretability and compliance with healthcare regulations.

3.1.2 Building a model to predict if a driver on Uber will accept a ride request or not
Explain how you'd structure the problem, select features, and handle class imbalance. Discuss how you would evaluate model performance and deploy the solution at scale.

3.1.3 Designing an ML system to extract financial insights from market data for improved bank decision-making
Outline how you would use APIs to source data, process it, and build models that deliver actionable insights. Mention considerations for data latency and integration with downstream systems.

3.1.4 Identify requirements for a machine learning model that predicts subway transit
Discuss data collection, feature selection, and how you'd address real-time prediction needs. Highlight your approach to handling missing data and evaluating model reliability.

3.1.5 Redesign batch ingestion to real-time streaming for financial transactions.
Describe the architecture changes needed for real-time ML, including data pipelines, model serving, and monitoring. Explain how you'd ensure low latency and data consistency.

3.2 Deep Learning & Model Selection

These questions probe your understanding of neural networks, advanced architectures, and the reasoning behind model selection. Expect to discuss both the theoretical underpinnings and practical applications relevant to medical data, signals, or imaging.

3.2.1 How does the transformer compute self-attention and why is decoder masking necessary during training?
Summarize the mechanics of self-attention and the purpose of masking in sequence models. Relate your explanation to scenarios where sequential prediction matters.

3.2.2 When you should consider using Support Vector Machine rather then Deep learning models
Compare the strengths and weaknesses of SVMs and deep learning for different data types and sample sizes. Justify your model choice based on interpretability, scalability, and data availability.

3.2.3 How would you explain neural networks to a child?
Demonstrate your ability to distill complex topics for non-technical audiences. Use analogies or simple examples to illustrate neural networks’ structure and function.

3.2.4 Kernel Methods
Describe what kernel methods are, how they work in the context of SVMs, and when you would use them. Touch on their application to non-linear problems.

3.2.5 Inception Architecture
Summarize the key components of the Inception network and why it’s effective for image data. Discuss how architectural innovations improve performance and efficiency.

3.3 Data Preparation & Feature Engineering

Globus Medical values meticulous data preparation and thoughtful feature engineering, especially when working with clinical or sensor data. These questions assess your ability to identify, clean, and transform data for optimal model performance.

3.3.1 Addressing imbalanced data in machine learning through carefully prepared techniques.
Explain approaches such as resampling, weighting, and synthetic data generation. Discuss how you would evaluate model performance under imbalance.

3.3.2 Describing a real-world data cleaning and organization project
Walk through a past experience cleaning messy data, detailing the specific steps and trade-offs you made. Emphasize reproducibility and communication with stakeholders.

3.3.3 Find the bigrams in a sentence
Describe your approach to n-gram extraction and how it could be used in text feature engineering. Mention considerations for efficiency and edge cases.

3.3.4 Write a function to get a sample from a standard normal distribution.
Explain the statistical reasoning and implementation steps for generating random samples. Discuss how you would validate the output.

3.4 Experimentation & Evaluation

You’ll be expected to design and evaluate experiments, particularly in regulated or high-stakes environments. Questions in this area focus on your ability to measure success, interpret results, and communicate findings.

3.4.1 The role of A/B testing in measuring the success rate of an analytics experiment
Describe how you would set up an A/B test, define success metrics, and ensure statistical validity. Discuss how you’d interpret and present the results.

3.4.2 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?
Outline your experimental design, including control/treatment groups and key performance indicators. Explain how you’d analyze the impact and account for confounding variables.

3.4.3 Write a function to get a sample from a Bernoulli trial.
Briefly explain Bernoulli processes and how you’d simulate trials in code. Discuss use cases for such simulations in model evaluation.

3.4.4 Use of historical loan data to estimate the probability of default for new loans
Describe your modeling approach, including choice of algorithm and evaluation metrics. Emphasize risk assessment and validation strategies.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision that impacted a business or product outcome.
Describe the context, the data analysis you performed, and how your insights influenced the final decision. Highlight measurable results and any follow-up actions.

3.5.2 Describe a challenging data project and how you handled it.
Focus on the obstacles you faced, how you structured your approach, and what you learned from the experience. Emphasize teamwork or resourcefulness if relevant.

3.5.3 How do you handle unclear requirements or ambiguity in project goals?
Discuss your method for clarifying objectives, communicating with stakeholders, and iterating on solutions. Show how you balance flexibility with delivering results.

3.5.4 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Explain your strategy for building consensus, using evidence, and addressing concerns. Include the outcome and what you learned about stakeholder management.

3.5.5 Give an example of how you balanced short-term wins with long-term data integrity when pressured to deliver quickly.
Describe how you prioritized deliverables, managed expectations, and ensured that shortcuts didn’t compromise future work.

3.5.6 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Detail your process for facilitating alignment, defining clear metrics, and communicating the importance of consistency.

3.5.7 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?
Explain your approach to missing data, how you communicated uncertainty, and how your findings were used.

3.5.8 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Highlight your ability to create visual or interactive tools that bridge communication gaps and accelerate consensus.

3.5.9 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?
Discuss your triage process, quality checks, and communication of caveats to leadership.

3.5.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Show how you identified the need for automation, implemented a solution, and measured its impact on workflow efficiency.

4.1 Company-specific tips:

• Familiarize yourself with Globus Medical’s product portfolio, especially its focus on spinal, orthopedic, and robotic surgical solutions. Understanding how machine learning can be applied to musculoskeletal disorders and surgical workflows will help you contextualize your technical answers.
• Research the regulatory environment for medical devices and healthcare technology. Be prepared to discuss how you would ensure model compliance with standards such as HIPAA, FDA guidelines, and data privacy regulations.
• Study recent innovations and published case studies from Globus Medical. Pay attention to how the company integrates advanced technology to improve patient care and surgical outcomes, and be ready to reference these in your interview.
• Develop a clear understanding of the challenges unique to healthcare data, such as data sparsity, imbalanced classes, and the importance of interpretability. Demonstrating awareness of these issues will set you apart as a candidate who understands the domain.

4.2 Role-specific tips:

4.2.1 Practice explaining your approach to risk assessment and predictive modeling for healthcare scenarios.
Prepare to discuss how you would build and validate machine learning models that evaluate patient health or predict outcomes, emphasizing your process for feature engineering, model selection, and ensuring interpretability. Highlight your strategies for handling imbalanced data and the steps you take to meet clinical standards.

4.2.2 Demonstrate your expertise in data preparation and cleaning with real-world examples.
Be ready to walk through specific projects where you cleaned, organized, and transformed messy clinical or sensor data. Detail the techniques you used to address missing values, outliers, and reproducibility, and explain how your work improved model performance or stakeholder trust.

4.2.3 Show your proficiency in designing and deploying end-to-end ML systems.
Expect questions that probe your ability to architect robust machine learning pipelines, from data ingestion to model serving and monitoring. Discuss how you would redesign batch ingestion to real-time streaming in a healthcare context, ensuring low latency and data consistency.

4.2.4 Prepare to compare and justify model choices, especially for medical data.
Review scenarios where you would select Support Vector Machines over deep learning models, or vice versa, based on sample size, interpretability, and data characteristics. Articulate your reasoning with confidence and tie your choices to healthcare-specific constraints.

4.2.5 Practice communicating complex ML concepts to non-technical audiences.
You’ll often need to explain neural networks, kernel methods, or transformer architectures to clinicians or product managers. Use analogies and simple language to demonstrate your ability to make technical topics accessible and relevant.

4.2.6 Review statistical concepts and experiment design, focusing on healthcare applications.
Brush up on A/B testing, success metrics, and evaluation strategies for analytics experiments. Prepare to outline how you would measure the impact of a new clinical tool or intervention, ensuring statistical validity and actionable insights.

4.2.7 Highlight your experience collaborating with cross-functional teams.
Reflect on past projects where you worked closely with software engineers, data scientists, and clinical experts. Be ready to discuss how you navigated unclear requirements, built consensus, and delivered results in ambiguous or high-stakes environments.

4.2.8 Prepare examples of automating data-quality checks and ensuring long-term data integrity.
Showcase your ability to identify recurrent data issues and implement automated solutions that prevent future crises, especially in regulated healthcare settings. Explain how you balanced speed with accuracy and measured the impact on workflow efficiency.

4.2.9 Be ready to address ethical considerations and data privacy in ML for healthcare.
Discuss how you would design models and workflows that respect patient privacy, mitigate bias, and align with Globus Medical’s commitment to safe and responsible innovation. Articulate your approach to ethical decision-making in the context of medical data.

4.2.10 Practice answering behavioral questions with measurable outcomes and clear communication.
Prepare stories where your data-driven insights led to tangible improvements in business or patient outcomes. Focus on your ability to communicate uncertainty, manage stakeholder expectations, and deliver executive-reliable results under pressure.

5.1 How hard is the Globus Medical ML Engineer interview?
The Globus Medical ML Engineer interview is considered challenging, especially for candidates new to healthcare or regulated industries. You’ll be evaluated on advanced machine learning concepts, hands-on coding, system design for healthcare applications, and your ability to communicate complex ideas to both technical and clinical audiences. Expect rigorous technical rounds and behavioral questions focused on collaboration and adaptability.

5.2 How many interview rounds does Globus Medical have for ML Engineer?
Typically, the process consists of 4–6 rounds: an initial application and resume review, recruiter screen, one or two technical/case interviews, a behavioral interview, and a final onsite or virtual round with senior engineers and stakeholders. Each round targets specific skill sets, including technical proficiency, model design, and cross-functional communication.

5.3 Does Globus Medical ask for take-home assignments for ML Engineer?
Yes, candidates may receive a take-home assignment or case study, often focused on building or evaluating a machine learning model relevant to healthcare data. These assignments are designed to assess your practical problem-solving skills, attention to detail in data preparation, and ability to communicate your approach clearly.

5.4 What skills are required for the Globus Medical ML Engineer?
Key skills include expertise in Python, SQL, and machine learning frameworks; experience with data preparation and cleaning; proficiency in designing, training, and deploying ML models; and a strong understanding of model evaluation and risk assessment. Familiarity with healthcare data, regulatory compliance, and effective communication with both technical and non-technical stakeholders is essential.

5.5 How long does the Globus Medical ML Engineer hiring process take?
Most candidates complete the process in 3–5 weeks from application to offer. Timelines may vary depending on candidate availability and team schedules, with technical and final rounds sometimes consolidated into a single day. Take-home assignments generally have a 3–5 day turnaround.

5.6 What types of questions are asked in the Globus Medical ML Engineer interview?
Expect a mix of technical and behavioral questions: machine learning system design for healthcare, deep learning and model selection, data cleaning and feature engineering, experiment design and evaluation, and scenarios requiring clear communication of complex concepts. Behavioral questions will probe your collaboration style, adaptability, and experience navigating ambiguity.

5.7 Does Globus Medical give feedback after the ML Engineer interview?
Globus Medical typically provides high-level feedback through recruiters. While detailed technical feedback may be limited, you can expect to hear about your overall performance and fit for the team.

5.8 What is the acceptance rate for Globus Medical ML Engineer applicants?
The role is highly competitive, with an estimated acceptance rate of 3–5% for qualified applicants. Globus Medical seeks candidates who demonstrate technical excellence, healthcare domain knowledge, and strong communication skills.

5.9 Does Globus Medical hire remote ML Engineer positions?
Yes, Globus Medical offers remote opportunities for ML Engineers, although some roles may require occasional onsite visits for team collaboration or project-specific needs. Flexibility depends on the team and project requirements.

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

With resources like the Globus Medical 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. Whether you’re preparing for machine learning system design, deep learning model selection, data preparation for healthcare, or behavioral rounds focused on cross-functional collaboration, you’ll find guides and examples directly relevant to what Globus Medical is looking for.

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