Grubhub AI Research Scientist Interview Guide

Getting ready for an AI Research Scientist interview at Grubhub? The Grubhub AI Research Scientist interview process typically spans a broad range of question topics and evaluates skills in areas like machine learning algorithms, experimental design, data-driven product optimization, and communicating complex technical concepts to diverse audiences. Excelling in this interview requires a strong grasp of both theoretical and applied AI, as well as the ability to translate research into practical solutions that directly impact Grubhub’s marketplace, personalization, and operational efficiency.

In preparing for the interview, you should:

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

Grubhub is the nation’s leading online and mobile food-ordering company, connecting diners with local takeout restaurants through a portfolio of brands including Grubhub, Seamless, MenuPages, Allmenus, DiningIn, and Restaurants on the Run. Serving over 28,800 restaurants across more than 600 U.S. cities and London, Grubhub streamlines the ordering process and supports every transaction with around-the-clock customer service. As an AI Research Scientist, you will contribute to advancing Grubhub’s technology, enhancing the efficiency and personalization of its platform to better serve diners and restaurant partners.

As an AI Research Scientist at Grubhub, you will develop advanced machine learning models and algorithms to enhance the platform’s food ordering, delivery logistics, and customer experience. You will work closely with engineering and product teams to research and prototype new AI solutions, such as recommendation systems, demand forecasting, and route optimization. Core responsibilities include analyzing large datasets, publishing research findings, and translating cutting-edge techniques into scalable applications. This role is crucial for driving innovation at Grubhub, ensuring the company leverages artificial intelligence to improve operational efficiency and deliver better service to users and restaurant partners.

2.1 Stage 1: Application & Resume Review

The process begins with an initial screening of your resume and application materials, typically by a recruiter or HR representative. For the AI Research Scientist role at Grubhub, particular attention is paid to your experience with machine learning, deep learning, natural language processing, recommendation systems, and practical deployments of AI solutions. Highlighting your research publications, industry impact, and proficiency in designing scalable models will help your application stand out. Prepare by tailoring your resume to showcase relevant projects, technical skills, and measurable outcomes in AI-driven environments.

2.2 Stage 2: Recruiter Screen

Next, you’ll have a brief call (20-30 minutes) with a recruiter focused on your motivations for joining Grubhub, alignment with the company’s mission, and your overall fit for the AI Research Scientist position. Expect questions about your career trajectory, interest in food delivery platforms, and ability to communicate complex technical concepts to non-technical stakeholders. Preparation should include a concise narrative of your background, familiarity with Grubhub’s business, and examples of bridging technical and business priorities.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or two interviews led by data science team members or AI research leads, lasting 45-60 minutes each. You’ll be assessed on your technical depth in areas such as neural networks, kernel methods, NLP, model evaluation techniques, and A/B testing. Expect to discuss case studies relevant to Grubhub’s platform, such as designing recommender systems for restaurants, evaluating user engagement metrics, or implementing multi-modal AI tools for content generation. Demonstrating your approach to real-world problems—such as bias mitigation, scalability, and actionable insights—is key. Preparation should include reviewing recent AI advancements, your past project details, and practicing articulating your problem-solving strategies.

2.4 Stage 4: Behavioral Interview

A behavioral interview is typically conducted by a hiring manager or a cross-functional team member and lasts 30-45 minutes. The focus is on your collaboration style, adaptability, communication skills, and ability to navigate challenges in data projects. You may be asked to describe experiences where you made data accessible to non-technical audiences, overcame hurdles in deploying machine learning models, or presented complex insights to business leaders. Prepare by reflecting on your past teamwork, stakeholder management, and how you’ve driven impact through research and experimentation.

2.5 Stage 5: Final/Onsite Round

The final round often consists of a half-day onsite (or virtual onsite) with 3-4 interviews. You’ll meet with senior data scientists, research managers, and product team members. This stage combines advanced technical questions, business case discussions, and deep dives into your previous research. You may be asked to whiteboard solutions, critique AI architectures, or design experiments to measure user experience improvements. You’ll also be evaluated on your ability to communicate findings, justify modeling choices, and propose innovative approaches for Grubhub’s AI-driven initiatives. Preparation should include reviewing your portfolio of work, anticipating cross-functional questions, and practicing clear, actionable presentations.

2.6 Stage 6: Offer & Negotiation

Once interviews are complete, the recruiter will reach out with feedback and, if successful, an offer. This stage involves discussing compensation, benefits, and potential team assignments. Negotiations are typically handled by the recruiter, and you may have an opportunity to meet with the hiring manager for final clarifications. Prepare by researching market compensation, understanding Grubhub’s benefits, and clarifying any role-specific expectations.

2.7 Average Timeline

The typical Grubhub AI Research Scientist interview process spans 3-5 weeks from initial application to offer. Fast-track candidates with exceptional research backgrounds or direct industry experience may progress in as little as 2 weeks, while the standard pace allows for one week between each stage to accommodate interview scheduling and case assignment reviews. Onsite rounds are usually scheduled within a week of technical interviews, and offer negotiations are completed promptly after final feedback.

Next, let’s dive into the types of interview questions you can expect throughout the Grubhub AI Research Scientist process.

3.1 Machine Learning & Model Design

Expect questions around designing, evaluating, and deploying machine learning models for real-world problems, especially those relevant to recommendation systems, prediction tasks, and generative AI. Focus on articulating your approach to model selection, feature engineering, bias mitigation, and experimentation.

3.1.1 How would you evaluate whether a 50% rider discount promotion is a good or bad idea? What metrics would you track and how would you implement it?
Frame your answer using experimental design, A/B testing, and business impact metrics such as conversion, retention, and profit margin. Discuss how you’d isolate the effect of the discount and monitor for unintended consequences.

3.1.2 How would you approach the business and technical implications of deploying a multi-modal generative AI tool for e-commerce content generation, and address its potential biases?
Highlight the importance of diverse training data, bias detection, and post-deployment monitoring. Address technical architecture, stakeholder alignment, and regulatory considerations.

3.1.3 Identify requirements for a machine learning model that predicts subway transit.
Discuss feature selection, data sources, temporal dependencies, and evaluation metrics. Emphasize scalability and robustness to noisy or incomplete data.

3.1.4 Building a model to predict if a driver on Uber will accept a ride request or not.
Describe your approach to binary classification, feature engineering (e.g., location, time, driver history), and handling class imbalance. Mention how you’d validate the model and monitor post-launch performance.

3.1.5 When should you consider using Support Vector Machine rather than Deep learning models?
Compare the strengths and weaknesses of SVMs versus deep learning, focusing on data size, interpretability, and computational resources. Use examples relevant to Grubhub’s business context.

3.1.6 Fine tuning vs RAG in chatbot creation.
Explain the trade-offs between custom model fine-tuning and retrieval-augmented generation pipelines. Discuss scalability, maintenance, and performance in production environments.

3.1.7 Design and describe key components of a RAG pipeline for a financial data chatbot system.
Outline the architecture, from document retrieval to response generation. Discuss how to ensure accuracy, relevance, and data privacy.

3.1.8 Implement logistic regression from scratch in code.
Break down the algorithm, emphasizing gradient descent, feature encoding, and code modularity. Discuss how you’d validate and test your implementation.

3.1.9 Describe kernel methods and their application.
Explain the intuition behind kernel methods, use cases for non-linear data, and how you’d select and tune kernels in a research setting.

3.2 Recommendation Systems & Search

These questions focus on building, evaluating, and refining recommendation engines and search features, which are core to product personalization and user engagement at Grubhub.

3.2.1 How would you design a restaurant recommender system?
Discuss collaborative filtering, content-based approaches, and hybrid models. Mention how you’d evaluate recommendations and handle cold start problems.

3.2.2 How would you improve the search feature on a large app?
Describe approaches for ranking, relevance, and personalization. Address how you’d measure success and iterate on the feature.

3.2.3 How would you analyze user journey data to recommend changes to the UI?
Focus on funnel analysis, segmentation, and behavioral clustering. Explain how you’d communicate actionable insights to product teams.

3.2.4 Designing a pipeline for ingesting media to built-in search within LinkedIn.
Outline the end-to-end pipeline, including preprocessing, indexing, and ranking. Highlight scalability and relevance for large datasets.

3.2.5 How would you build a recommendation engine for TikTok’s FYP algorithm?
Discuss sequence modeling, diversity, feedback loops, and evaluation strategies. Relate your answer to similar recommendation challenges at Grubhub.

3.3 Natural Language Processing & Data Representation

Expect questions on extracting insights from text, designing NLP pipelines, and visualizing complex data distributions. Emphasize your ability to handle real-world, noisy data and communicate findings.

3.3.1 How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?
Describe techniques such as word clouds, frequency histograms, and clustering. Explain how you’d tailor visualizations for different audiences.

3.3.2 Find the bigrams in a sentence.
Explain your approach to tokenization, edge cases, and performance optimization for large datasets.

3.3.3 How would you conduct sentiment analysis on WallStreetBets posts?
Discuss preprocessing, lexicon-based vs. machine learning approaches, and how you’d validate sentiment accuracy.

3.3.4 How would you match FAQs to user queries?
Describe embedding techniques, similarity metrics, and evaluation strategies for matching accuracy.

3.3.5 How would you extract the top N frequent words from a dataset?
Detail your approach to preprocessing, counting, and optimizing for scalability.

3.4 Experimentation & Data Analysis

You’ll be asked about designing experiments, measuring success, and interpreting results in dynamic business environments. Prioritize clarity in your experimental setup and statistical rigor.

3.4.1 The role of A/B testing in measuring the success rate of an analytics experiment.
Outline how to set up control and treatment groups, measure lift, and ensure statistical validity.

3.4.2 Assessing the market potential and then use A/B testing to measure its effectiveness against user behavior.
Discuss hypothesis formulation, experimental design, and post-analysis recommendations.

3.4.3 How do you bootstrap a data set?
Explain resampling techniques, their use in estimating uncertainty, and practical applications.

3.4.4 How would you evaluate a decision tree model?
Describe metrics such as accuracy, precision, recall, and overfitting mitigation strategies.

3.4.5 How would you analyze how a feature is performing?
Highlight the use of key metrics, cohort analysis, and user feedback for iterative improvement.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision, and what impact it had on the business or project.

3.5.2 Describe a challenging data project and how you handled obstacles or unexpected issues.

3.5.3 How do you handle unclear requirements or ambiguity in a project, especially when working with cross-functional teams?

3.5.4 Share an example of when you resolved a conflict with a colleague or stakeholder regarding a data-driven recommendation.

3.5.5 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a solution quickly.

3.5.6 Describe a situation where you had to influence stakeholders without formal authority to adopt your analysis or recommendation.

3.5.7 Tell me about a time you delivered critical insights even though a significant portion of your dataset had missing or unreliable values. What trade-offs did you make?

3.5.8 Walk us through how you built a quick-and-dirty de-duplication script on an emergency timeline.

3.5.9 How do you prioritize multiple deadlines and stay organized when several projects compete for your attention?

3.5.10 Share a story where you used data prototypes or wireframes to align stakeholders with different visions of the final deliverable.

4.1 Company-specific tips:

Demonstrate a deep understanding of Grubhub’s business model, especially how AI can drive value in food delivery logistics, restaurant recommendations, and user personalization. Familiarize yourself with the core challenges of the on-demand food marketplace, such as optimizing delivery times, matching supply and demand, and enhancing customer retention. Be prepared to discuss how AI solutions can improve operational efficiency and create seamless dining experiences for both diners and restaurant partners.

Stay up-to-date on Grubhub’s latest technological initiatives, such as advancements in their mobile app, new personalization features, or logistics optimizations. Reference recent product launches, partnerships, or industry trends that could benefit from advanced AI research. This shows your genuine interest in Grubhub’s growth and your ability to align your expertise with their strategic direction.

Understand the impact of AI-driven experimentation at Grubhub. Highlight your experience with A/B testing, causal inference, and business metric optimization, especially as they relate to real-time decision-making in dynamic environments. Be ready to discuss how you would structure experiments to measure the impact of new features, promotions, or algorithm changes on Grubhub’s key performance indicators.

4.2 Role-specific tips:

Showcase your expertise in designing, evaluating, and deploying machine learning models tailored to large-scale, real-world problems. Emphasize your ability to select the right algorithms—whether it’s deep learning for personalization, kernel methods for non-linear data, or classical models for interpretability—and justify your choices in the context of Grubhub’s platform. Prepare to articulate how you address challenges like data sparsity, class imbalance, and scalability in production environments.

Highlight your hands-on experience with recommendation systems and search optimization. Be prepared to discuss the design and evaluation of restaurant recommenders, hybrid collaborative/content-based models, and strategies for tackling cold start problems. Explain how you would measure the effectiveness of recommendations and iterate on models to drive user engagement and satisfaction.

Demonstrate a strong command of natural language processing techniques relevant to Grubhub’s user interactions, such as extracting insights from reviews, matching FAQs to user queries, and performing sentiment analysis. Practice explaining your approach to tokenization, embedding techniques, and scalable text processing. Be ready to discuss how to visualize long-tail text data and communicate actionable insights to both technical and business stakeholders.

Show your proficiency in experimental design and statistical analysis. Prepare to outline how you would set up A/B tests to evaluate new features, promotions, or model changes, ensuring statistical rigor and business relevance. Discuss your approach to measuring lift, controlling for confounding variables, and making data-driven recommendations based on experiment outcomes.

Be ready to code and explain foundational algorithms from scratch, such as logistic regression or decision trees. Practice breaking down complex algorithms into modular, testable components, and discuss your strategies for validating and stress-testing your implementations. This demonstrates your technical depth and attention to detail, which are critical for a research-focused role.

Prepare compelling stories about your past research and its real-world impact. Use the STAR (Situation, Task, Action, Result) framework to describe how you’ve led projects, overcome obstacles, and delivered business value through AI innovation. Emphasize your ability to translate research findings into scalable, production-ready solutions that align with organizational goals.

Demonstrate your ability to communicate complex technical concepts clearly to non-technical audiences. Practice explaining your research, modeling choices, and experimental results in a way that resonates with cross-functional teams, including product managers, engineers, and business stakeholders. Effective communication is essential for driving alignment and delivering impactful AI solutions at Grubhub.

5.1 How hard is the Grubhub AI Research Scientist interview?
The Grubhub AI Research Scientist interview is intellectually rigorous and multifaceted. Candidates are assessed on advanced machine learning theory, hands-on algorithmic design, experimentation, and the ability to translate research into practical solutions for Grubhub’s food delivery marketplace. You’ll need to demonstrate deep technical expertise, creativity in problem-solving, and clear communication with both technical and non-technical stakeholders. If you thrive in both academic and applied AI environments, you’ll find the challenge rewarding.

5.2 How many interview rounds does Grubhub have for AI Research Scientist?
Grubhub typically conducts 5-6 rounds for the AI Research Scientist position. This includes an initial recruiter screen, technical interviews with data science and research team members, a behavioral interview, and a final onsite (or virtual onsite) with multiple stakeholders such as senior scientists and product managers. Each round is designed to evaluate a different facet of your expertise and fit for the team.

5.3 Does Grubhub ask for take-home assignments for AI Research Scientist?
Yes, Grubhub may include a take-home assignment or case study, especially in the technical round. These assignments often involve designing machine learning models, analyzing experimental results, or proposing solutions to real business challenges—such as recommendation system design or A/B testing frameworks. The goal is to assess your approach to open-ended problems and your ability to communicate actionable insights.

5.4 What skills are required for the Grubhub AI Research Scientist?
Essential skills for this role include deep knowledge of machine learning algorithms, experimental design, natural language processing, recommendation systems, and statistical analysis. You should be proficient in coding (Python, R, or similar), comfortable with large-scale data analysis, and experienced in translating research into scalable applications. Strong communication and collaboration skills are also critical, as you’ll work across teams to drive innovation.

5.5 How long does the Grubhub AI Research Scientist hiring process take?
The typical timeline for the Grubhub AI Research Scientist hiring process is 3-5 weeks from application to offer. Fast-track candidates with notable research backgrounds or direct industry experience may move quicker, while scheduling and case assignment reviews can extend the process. Grubhub aims for prompt feedback and efficient scheduling throughout.

5.6 What types of questions are asked in the Grubhub AI Research Scientist interview?
You’ll encounter a mix of technical, case-based, and behavioral questions. Technical questions cover topics like neural networks, kernel methods, NLP, recommendation systems, and experimental design. Case studies often focus on Grubhub-specific challenges, such as optimizing delivery logistics or personalizing restaurant recommendations. Behavioral questions assess your ability to collaborate, adapt, and communicate complex concepts to diverse audiences.

5.7 Does Grubhub give feedback after the AI Research Scientist interview?
Grubhub generally provides high-level feedback through recruiters after each interview stage. While detailed technical feedback may be limited, you’ll receive guidance on your strengths and areas for improvement. If you reach the final round, you may have an opportunity to discuss feedback directly with the hiring manager.

5.8 What is the acceptance rate for Grubhub AI Research Scientist applicants?
While Grubhub does not publicly share acceptance rates, the AI Research Scientist role is highly competitive, with an estimated acceptance rate below 5%. Candidates with a strong blend of research excellence, industry impact, and practical machine learning experience stand out in the process.

5.9 Does Grubhub hire remote AI Research Scientist positions?
Yes, Grubhub does offer remote opportunities for AI Research Scientists, particularly for candidates with specialized expertise or exceptional research backgrounds. Some roles may require occasional travel to Grubhub’s offices for team collaboration or key project milestones, but remote work is supported for many positions.

• Related guides:
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  • AI Research Scientist interview guide
  • Top artificial intelligence interview tips

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