Freshdesk ML Engineer Interview Guide

Getting ready for an ML Engineer interview at Freshdesk? The Freshdesk ML Engineer interview process typically spans a wide range of question topics and evaluates skills in areas like machine learning system design, data analytics, probability, model implementation, and communicating technical insights to varied audiences. Interview preparation is especially important for this role at Freshdesk, as candidates are expected to demonstrate both technical depth and the ability to solve real-world business challenges with scalable, reliable ML solutions that enhance customer support and operational efficiency.

In preparing for the interview, you should:

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

Freshdesk, part of Freshworks Inc., is a leading provider of cloud-based customer support software designed to help businesses manage and streamline customer interactions. Serving organizations of all sizes across various industries, Freshdesk offers solutions for ticketing, automation, and omnichannel support to improve customer satisfaction and operational efficiency. The company emphasizes usability, scalability, and innovation in customer experience management. As an ML Engineer, you will contribute to developing intelligent features that enhance automation and deliver smarter support solutions, directly supporting Freshdesk’s mission to simplify and elevate customer service.

As an ML Engineer at Freshdesk, you will design, develop, and implement machine learning models to enhance customer support solutions and automate workflows. You’ll collaborate closely with product managers, data scientists, and software engineers to identify opportunities for AI-driven improvements, such as intelligent ticket routing, sentiment analysis, and chatbots. Your key responsibilities include processing large datasets, building scalable ML pipelines, and deploying models into production environments. By leveraging advanced algorithms and data-driven insights, you contribute to improving customer experience and operational efficiency, supporting Freshdesk’s mission to deliver smarter, more responsive support tools.

2.1 Stage 1: Application & Resume Review

The process begins with an initial screening of your application materials, focusing on your experience with machine learning (ML), analytics, probability, and technical problem-solving. The review is conducted by a member of the recruiting team or a technical hiring manager, who evaluates your background in implementing ML models, working with large datasets, and leveraging statistical analysis to solve business problems. To prepare, ensure your resume highlights hands-on ML engineering projects, familiarity with model evaluation, and proficiency in programming and data manipulation.

2.2 Stage 2: Recruiter Screen

If your profile matches the requirements, you'll be contacted for a short call with a recruiter. This conversation typically lasts 20–30 minutes and centers on your motivation for joining Freshdesk, your understanding of the ML Engineer role, and your overall fit for the company culture. The recruiter may also ask about your previous experiences, communication skills, and ability to translate technical insights for non-technical stakeholders. Preparation should include a clear articulation of your career goals, reasons for interest in Freshdesk, and examples of your collaborative and communication strengths.

2.3 Stage 3: Technical/Case/Skills Round

This stage comprises one or two technical interviews, each lasting 45–60 minutes, conducted by senior ML engineers or data scientists. You can expect a mix of live coding, algorithmic problem-solving, and ML system design questions. Topics often include implementing core ML algorithms (such as logistic regression), probability-based reasoning, data cleaning, and analytics case studies. You may also be asked to design scalable ML systems, discuss model evaluation metrics, or demonstrate your ability to build solutions for real-world business scenarios (e.g., sentiment analysis, recommendation engines, or predictive modeling). Preparation should involve practicing implementation of ML algorithms from scratch, reviewing probability concepts, and thinking through end-to-end ML pipelines.

2.4 Stage 4: Behavioral Interview

The behavioral interview, often led by an HR representative or a hiring manager, explores your approach to teamwork, project management, and overcoming challenges in data projects. You may be asked to reflect on past experiences where you tackled complex analytics problems, dealt with ambiguous requirements, or communicated technical results to diverse audiences. Focus on providing structured, concise responses using the STAR (Situation, Task, Action, Result) method and emphasizing adaptability, stakeholder management, and your commitment to continuous learning.

2.5 Stage 5: Final/Onsite Round

The final round may involve a panel interview or a series of back-to-back sessions with cross-functional team members, including engineering leads and analytics directors. This stage assesses both depth and breadth of your ML expertise, your ability to collaborate with product and engineering teams, and your strategic thinking around deploying ML solutions at scale. You might be asked to walk through a prior project, critique an ML system, or brainstorm improvements to an existing workflow. Preparation should include revisiting your portfolio, practicing clear and confident technical communication, and anticipating scenario-based questions.

2.6 Stage 6: Offer & Negotiation

If you successfully navigate the previous stages, the recruiter will reach out with a formal offer. This discussion covers compensation, benefits, start date, and any outstanding questions about the team or company. Be prepared to negotiate thoughtfully, backed by research on industry standards and your unique value proposition.

2.7 Average Timeline

The typical Freshdesk ML Engineer interview process spans 2–4 weeks from initial application to offer. Fast-track candidates with highly relevant experience and prompt scheduling may complete the process in as little as 10–14 days, while standard pacing—with time for technical assessments and panel coordination—usually results in a three-week timeline.

Next, let’s dive into the types of interview questions you can expect during the Freshdesk ML Engineer interview process.

3.1 Machine Learning System Design

Expect questions that probe your ability to architect robust ML systems for real-world applications, including handling complex data flows and making design trade-offs. You’ll need to demonstrate familiarity with model evaluation, scalability, and aligning technical choices with business objectives.

3.1.1 Identify requirements for a machine learning model that predicts subway transit
Break down the problem by outlining key features, data sources, and evaluation metrics. Discuss how you’d handle missing data and incorporate real-time signals for accurate predictions.

3.1.2 Designing an ML system for unsafe content detection
Explain your approach to data labeling, model selection, and deployment for content moderation. Mention how you’d balance precision and recall, and ensure ethical and privacy safeguards.

3.1.3 Design and describe key components of a RAG pipeline
Describe the retrieval-augmented generation pipeline, specifying how you’d integrate retrieval models and generative models. Address challenges in latency, scalability, and evaluation.

3.1.4 Fine Tuning vs RAG in chatbot creation
Compare the benefits and limitations of fine-tuning versus retrieval-augmented generation for building chatbots. Discuss criteria for choosing one approach over the other based on business needs.

3.1.5 Building a model to predict if a driver on Uber will accept a ride request or not
Lay out the problem as a classification task, specifying feature engineering, handling class imbalance, and evaluation metrics. Mention how you’d validate the model’s impact on operational efficiency.

3.2 Probability & Statistical Modeling

You’ll encounter questions that assess your knowledge of probability, inference, and statistical rigor in analyzing real-world scenarios. Be ready to discuss experimental design, hypothesis testing, and uncertainty quantification.

3.2.1 Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today
Frame the problem using Markov chains or conditional probabilities, and explain your recursive or iterative approach to calculation.

3.2.2 Use of historical loan data to estimate the probability of default for new loans
Describe how you’d use maximum likelihood estimation, feature analysis, and validation techniques. Discuss how you’d communicate risk estimates to non-technical stakeholders.

3.2.3 Expected Tests
Apply probability theory to estimate the expected number of tests needed for a given scenario. Be clear about assumptions and edge cases.

3.2.4 Ad raters are careful or lazy with some probability
Model user behavior using probabilistic frameworks, and discuss how you’d validate the assumptions. Explain how you’d use results to improve system reliability.

3.2.5 Disease Testing Probability
Describe how you’d use Bayes’ theorem to calculate false positive and false negative rates. Discuss implications for decision-making in health or safety-critical applications.

3.3 Data Analytics & Feature Engineering

These questions test your ability to extract actionable insights from data and build features that improve model performance. Focus on practical approaches for cleaning, transforming, and visualizing data.

3.3.1 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets
Detail your process for profiling, cleaning, and restructuring raw data to enable reliable analytics. Discuss how you’d automate recurring data-cleaning tasks.

3.3.2 Design a feature store for credit risk ML models and integrate it with SageMaker
Explain the architecture of a robust feature store, focusing on scalability, versioning, and integration with ML pipelines.

3.3.3 Write a function to return a new list where all empty values are replaced with the most recent non-empty value in the list
Describe your approach to handling missing data in time-series or sequential datasets, emphasizing reproducibility and efficiency.

3.3.4 Write a function to get a sample from a standard normal distribution
Explain how to leverage statistical libraries or algorithms to generate random samples, and discuss use cases for simulation or model validation.

3.3.5 Write a function that splits the data into two lists, one for training and one for testing
Discuss strategies for splitting data to avoid leakage and ensure representative samples, especially when working outside common libraries.

3.4 Business Impact & Experimentation

Expect questions that probe your ability to translate ML outputs into business value and design experiments that drive strategic decisions. Articulate how you measure success and adapt models to evolving requirements.

3.4.1 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?
Lay out an experimental framework, including A/B testing, key metrics, and confounding factors. Explain how you’d analyze the impact on user retention and profitability.

3.4.2 Let's say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU)
Describe how you’d design experiments to test product features, track DAU, and identify leading indicators of growth.

3.4.3 How to model merchant acquisition in a new market?
Discuss your approach to modeling acquisition funnels, segmenting prospects, and measuring campaign effectiveness.

3.4.4 How would you analyze how the feature is performing?
Explain the process for defining KPIs, collecting relevant data, and running statistical tests to evaluate feature success.

3.4.5 Delivering an exceptional customer experience by focusing on key customer-centric parameters
Describe how you’d identify customer-centric metrics, design feedback loops, and use analytics to drive continuous improvement.

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 influenced a business choice, and highlight the metrics or insights you presented to stakeholders.

3.5.2 Describe a challenging data project and how you handled it.
Walk through the complexity, how you overcame obstacles, and the final impact of your work on the team or company.

3.5.3 How do you handle unclear requirements or ambiguity?
Share your approach to clarifying objectives, communicating with stakeholders, and adapting your analysis as new information emerges.

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?
Explain how you facilitated dialogue, presented evidence, and reached consensus or compromise.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Show how you quantified additional work, prioritized requests, and maintained project integrity through structured communication.

3.5.6 When leadership demanded a quicker deadline than you felt was realistic, what steps did you take to reset expectations while still showing progress?
Discuss your strategy for communicating risks, re-scoping deliverables, and maintaining trust with leadership.

3.5.7 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Describe the trade-offs you made, how you documented caveats, and your plan for future improvements.

3.5.8 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Explain your persuasion techniques, use of prototypes or pilots, and how you measured success.

3.5.9 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Share your prioritization framework, communication strategy, and how you managed competing interests.

3.5.10 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your approach to handling missing data, quantifying uncertainty, and ensuring stakeholders understood the limitations of your analysis.

4.1 Company-specific tips:

Familiarize yourself with Freshdesk’s core product offerings, especially around customer support automation and omnichannel ticketing. Understanding how machine learning can enhance features such as intelligent ticket routing, sentiment analysis, and customer experience will help you align your answers with Freshdesk’s mission.

Research recent innovations at Freshdesk, such as AI-powered chatbots and workflow automation. Be ready to discuss how you would leverage ML to improve these features or introduce new ones that drive operational efficiency and customer satisfaction.

Review Freshdesk’s values around usability, scalability, and innovation. Prepare examples that showcase your ability to build ML solutions that are not only technically robust but also accessible and impactful for end-users in a customer service context.

4.2 Role-specific tips:

Demonstrate expertise in ML system design for real-world customer support scenarios.
Practice breaking down open-ended problems into clear requirements, data sources, and model evaluation metrics. Be prepared to discuss how you would architect scalable ML pipelines for tasks like ticket classification, prioritization, and real-time sentiment analysis, emphasizing reliability and adaptability.

Show proficiency in implementing and optimizing core ML algorithms from scratch.
Be ready to code algorithms such as logistic regression, decision trees, or clustering methods during live interviews. Focus on explaining your thought process, handling edge cases, and optimizing for performance, especially when working with large or noisy datasets typical in support environments.

Highlight your ability to clean, transform, and engineer features from messy support data.
Prepare to discuss strategies for profiling and restructuring raw data, such as ticket logs or chat transcripts. Demonstrate how you automate data cleaning tasks, engineer meaningful features, and handle missing values to improve model accuracy and robustness.

Display strong statistical reasoning and probability skills in business-relevant contexts.
Review concepts like hypothesis testing, uncertainty quantification, and Markov chains. Be ready to apply these frameworks to evaluate experiments, estimate probabilities (e.g., ticket resolution rates), and communicate risk or confidence levels to non-technical stakeholders.

Communicate technical insights clearly to cross-functional audiences.
Practice explaining complex ML concepts and model results in simple, actionable terms. Prepare examples of how you’ve translated technical findings into business decisions, addressed stakeholder concerns, and influenced product strategy through data-driven insights.

Showcase experience with end-to-end ML deployment and monitoring.
Discuss your approach to deploying models in production, integrating with existing support workflows, and setting up monitoring for drift or performance issues. Highlight how you ensure models remain reliable and relevant over time, adapting to evolving customer needs.

Prepare to discuss the business impact of your ML solutions.
Be ready to articulate how your work drives measurable improvements in customer satisfaction, operational efficiency, or feature adoption. Use concrete metrics and experiment design frameworks to demonstrate your ability to evaluate and iterate on ML-driven product enhancements.

Demonstrate adaptability and problem-solving in ambiguous or fast-changing environments.
Share stories of how you handled unclear requirements, scope creep, or shifting priorities in past projects. Emphasize your proactive communication, structured decision-making, and commitment to maintaining both short-term impact and long-term data integrity.

Highlight collaboration with product managers, engineers, and data scientists.
Prepare examples of cross-functional teamwork, especially where you identified opportunities for ML-driven improvements, aligned technical solutions with business goals, and navigated differing perspectives to deliver successful outcomes.

Practice behavioral interview responses using the STAR method.
Craft concise stories that showcase your leadership, resilience, and ability to drive results in challenging situations. Focus on your approach to stakeholder management, continuous learning, and delivering value even when faced with messy data or tight deadlines.

5.1 How hard is the Freshdesk ML Engineer interview?
The Freshdesk ML Engineer interview is challenging, focusing on both technical depth and practical problem-solving. You’ll be tested on your ability to design robust ML systems, analyze messy datasets, and communicate insights to diverse audiences. Expect a mix of algorithmic coding, machine learning system design, probability-based reasoning, and business impact questions. Candidates who demonstrate clear thinking, strong fundamentals, and relevance to customer support automation stand out.

5.2 How many interview rounds does Freshdesk have for ML Engineer?
Typically, there are 5–6 rounds: an initial resume screen, recruiter call, one or two technical interviews, a behavioral interview, and a final onsite or panel round. Each stage is designed to assess different aspects of your expertise, from hands-on ML implementation to collaboration and communication skills.

5.3 Does Freshdesk ask for take-home assignments for ML Engineer?
Freshdesk may occasionally include a take-home assignment, especially for candidates who need to demonstrate practical skills in data cleaning, feature engineering, or model building. However, most technical evaluation is conducted through live coding and case-based interviews.

5.4 What skills are required for the Freshdesk ML Engineer?
Key skills include machine learning system design, algorithm implementation (such as logistic regression or clustering), probability and statistical analysis, data engineering, feature creation, and model deployment. Strong communication and the ability to translate technical results into business impact are crucial, as is experience working with large, messy customer support datasets.

5.5 How long does the Freshdesk ML Engineer hiring process take?
The process typically takes 2–4 weeks from application to offer. Fast-track candidates may complete all rounds in as little as 10–14 days, while standard pacing allows time for technical assessments and panel scheduling.

5.6 What types of questions are asked in the Freshdesk ML Engineer interview?
Expect a blend of live coding (algorithm implementation and data manipulation), ML system design (scalability, evaluation, and deployment), probability and statistics (Markov chains, hypothesis testing), business impact scenarios, and behavioral questions about teamwork, adaptability, and communication.

5.7 Does Freshdesk give feedback after the ML Engineer interview?
Freshdesk usually provides high-level feedback through recruiters, focusing on strengths and areas for improvement. Detailed technical feedback may vary depending on interviewer availability and company policy.

5.8 What is the acceptance rate for Freshdesk ML Engineer applicants?
While specific rates aren’t published, the ML Engineer role at Freshdesk is highly competitive, with an estimated acceptance rate of 3–6% for well-qualified applicants. Strong alignment with Freshdesk’s mission and demonstrated expertise in customer support automation can improve your odds.

5.9 Does Freshdesk hire remote ML Engineer positions?
Yes, Freshdesk offers remote opportunities for ML Engineers, with flexibility for hybrid or remote-first arrangements depending on the team and project requirements. Some roles may require occasional office visits for collaboration or onboarding.

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

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

With resources like the Freshdesk 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!