Decagon Research Scientist Interview Guide 2026

What is a Research Scientist at Decagon?

As a Research Scientist at Decagon, you play a pivotal role in shaping the future of conversational AI. This position is essential for advancing our mission to empower brands with intelligent, human-like customer experiences that can scale across various platforms. Your contributions will directly impact the development of AI agents that not only understand complex contexts but also exhibit genuine empathy while resolving customer inquiries. The work you do here will help redefine customer support, enabling brands like Hertz, Duolingo, and Eventbrite to deliver seamless experiences at an unprecedented scale.

In this role, you will be part of a dynamic Research team known for its innovative approaches to building AI systems that tackle challenges previously deemed impossible. You will design and implement cutting-edge methods for instruction tuning and information retrieval, making your work both technically challenging and strategically influential. The projects you undertake will not only enhance Decagon's product offerings but also improve customer satisfaction on a global scale, thus playing a crucial part in our continued growth and success.

Common Interview Questions

As you prepare for your interview, expect questions that reflect the diverse skills and experiences necessary for a Research Scientist role. The questions listed here are representative of what candidates have encountered in the past and are drawn from 1point3acres.com. They will illustrate key themes rather than serve as a memorization list.

Technical / Domain Questions

This category assesses your technical expertise and your familiarity with AI/ML concepts.

Explain the process of fine-tuning a large language model (LLM).
What are the key differences between supervised, unsupervised, and reinforcement learning?
Describe a challenging AI/ML project you've worked on from concept to deployment.
How do you ensure that your models are robust and generalizable?
Discuss the importance of data preprocessing in training AI models.

Problem-Solving / Case Studies

Expect to demonstrate your analytical skills and your approach to solving complex problems.

How would you approach a scenario where your model's performance suddenly degrades?
Given a dataset with missing values, what strategies would you employ to handle them?
Describe a time when you had to pivot your approach due to unexpected results.

Behavioral / Leadership

This section evaluates your soft skills and cultural fit with Decagon.

How do you handle disagreements within a team?
Describe a situation where you demonstrated leadership in a project.
What motivates you in your work, and how do you share that motivation with your team?

System Design / Architecture

You may be asked to design systems that support scalable AI solutions.

Outline a high-level architecture for a multi-modal AI system.
What considerations would you make for deploying AI models at scale?
How would you integrate user feedback into the model improvement cycle?

Coding / Algorithms

If applicable, be prepared to showcase your coding skills and algorithmic thinking.

Write a function to evaluate the accuracy of a classification model.
How would you implement a data pipeline for a real-time AI application?
Discuss the trade-offs of various algorithms for a given problem.

See every interview question for this role

Practice questions from our question bank

Curated questions for Decagon from real interviews. Click any question to practice and review the answer.

Medium

NLP

Compare Positional Encodings in Transformers

Implement and compare sinusoidal vs learned positional encodings in a Transformer for legal clause classification where word order changes meaning.

Tokenization

Language Models

Deep Learning

Medium

Statistics & Probability

Root-Cause a Failing QC Assay

Use normal/t-tests and a lot-comparison Welch test to decide if a QC assay failure indicates a true mean shift or a bad reagent lot.

Hypothesis Testing

Statistical Significance

Experimentation

Hard

Model Evaluation

Evaluate MIMO Channel Estimation Errors

Assess how rising channel estimation error in a 4x4 MIMO system drives BER, outage, and throughput degradation, and recommend fixes.

Accuracy

Calibration

Easy

NLP

Classify RL-LLM Research Interests

Build a transformer-based multi-label classifier to categorize candidate statements about RL and LLM research interests into recruiting themes.

Tokenization

Language Models

Deep Learning

Medium

Machine Learning

Predict Turbine Failures from Sensor Data

Build a gradient-boosted classifier to predict turbine failures 7 days ahead from sensor, alarm, and maintenance history data.

Supervised Learning

Cross-Validation

Feature Engineering

Medium

Machine Learning

Harden Vision Models Against Adversarial Attacks

Train a CNN for traffic-sign classification, measure adversarial robustness with FGSM, and improve deployed-model resilience with adversarial training.

Regularization

Neural Networks

Deep Learning

Medium

Machine Learning

Rank Human Preferences for RLHF

Train a pairwise preference model for RLHF that predicts which LLM response humans prefer and produces deployable reward scores.

Supervised Learning

Regularization

Deep Learning

Hard

Machine Learning

Conditioned Reverse Diffusion Process for User Prompts

Develop a reverse diffusion model conditioned on user editing prompts for text generation tasks.

Neural Networks

Gradient Descent

Deep Learning

Easy

Coding

Explain Solution Time Complexity

Explain how to analyze a coding solution's time complexity by counting operations, loops, and data structure costs.

Arrays

Sorting

Searching

Hard

NLP

Explain Transformer Architecture and Attention Mechanisms

Discuss the architecture of Transformers, focusing on self-attention and its impact on NLP tasks.

Neural Networks

Language Models

Deep Learning

Medium

Machine Learning

Build a Multimodal LLM for Real-Time Video Editing

Develop a multimodal large language model to enhance user experience in real-time video editing applications.

Supervised Learning

Deep Learning

Feature Engineering

Easy

Statistics & Probability

Explain P-Value in Marketing Test

Use a two-proportion z-test to assess a banner A/B test, then explain the resulting p-value clearly to a non-technical stakeholder.

Hypothesis Testing

Confidence Intervals

P-Values

Easy

Statistics & Probability

P-Value and Power for Button Redesign

Use a two-proportion z-test and power analysis to explain whether a 1-point signup lift from a button redesign is statistically credible.

Hypothesis Testing

P-Values

Power Analysis

Easy

Model Evaluation

Choose RMSE vs MAE

Compare two rent prediction models and decide whether MAE or RMSE is the better selection metric given costly large errors.

Regression

RMSE

MAE

Easy

Model Evaluation

Choose RMSE or MAE

Explain RMSE vs MAE using two rent prediction models and recommend which metric and model better fit a business sensitive to large errors.

Regression

RMSE

MAE

Easy

Statistics & Probability

Confidence Interval for Email CTR Test

Construct and interpret a 95% confidence interval for the CTR lift in an email A/B test to communicate uncertainty in the experiment result.

Confidence Intervals

Statistical Significance

Experimentation

Easy

Statistics & Probability

Explaining P-Value and Power

Compute a two-proportion z-test and explain p-value and statistical power for an onboarding experiment with an inconclusive result.

Hypothesis Testing

P-Values

Power Analysis

Hard

Coding

Serialize and Deserialize Binary Tree

Serialize a binary tree to a string and reconstruct it exactly using preorder traversal with null markers.

Trees

Recursion

Queue

Hard

Product Sense

Design Instagram Reels Recommendations

Design Instagram Reels recommendations to maximize relevance and satisfaction while balancing creator discovery, content quality, and ecosystem health.

User Needs

Use Cases

Product Vision

Easy

Execution

Scale a Model Experiment

Plan an 8-week path to scale a promising OpenAI model experiment under tight compute, evaluation, and shared-cluster constraints.

Trade-offs

Success Criteria

Risk Assessment

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Getting Ready for Your Interviews

Preparation for your interview should focus on both your technical capabilities and your alignment with Decagon's values. Understanding the company's mission and the specific demands of the Research Scientist role will enhance your responses and demonstrate your commitment to the role.

Role-related knowledge – This criterion emphasizes your technical expertise in AI/ML systems. Interviewers will evaluate your depth of knowledge and practical experience. Be prepared to discuss specific tools, technologies, and methodologies you have used.

Problem-solving ability – You will be assessed on how you approach complex challenges. Interviewers look for structured thinking and creativity in your solutions. Use the STAR method (Situation, Task, Action, Result) to frame your answers effectively.

Leadership – Demonstrating your ability to influence and communicate with others will be critical. Highlight experiences where you have led projects or collaborated across teams, showcasing your interpersonal skills.

Culture fit / values – Understanding and aligning with Decagon's core values will be essential. Be ready to discuss how you embody traits like a relentless momentum, customer focus, and teamwork in your past experiences.

Interview Process Overview

The interview process at Decagon is designed to rigorously assess both your technical skills and cultural fit. Candidates can expect a structured yet dynamic series of evaluations that reflect the company's commitment to excellence and innovation. The process typically begins with an initial screening, followed by a series of technical interviews focusing on your AI/ML expertise and problem-solving abilities.

Throughout the interview, expect an emphasis on collaboration and user-centric thinking. Decagon values candidates who can articulate their thought processes clearly and demonstrate how they would contribute to the company's mission. The pace is fast, reflecting the company's growth mindset and drive for impactful results.

The visual timeline provides an overview of the interview stages, highlighting the balance of technical and behavioral assessments. Use this to plan your preparation, ensuring that you allocate time to brush up on both technical skills and cultural alignment. Be aware that variations may occur depending on the specific team or role level.

Deep Dive into Evaluation Areas

To excel in your interviews, it's crucial to understand how Decagon evaluates candidates across several key areas.

Technical Proficiency

Your technical skills form the foundation of your candidacy. Interviewers will assess your understanding of AI/ML principles and your ability to apply them effectively. Strong performance in this area includes demonstrating familiarity with current technologies and frameworks.

Be ready to go over:

Model Training Techniques – Understanding of various training methodologies and their applications.
Data Handling – Proficiency in preprocessing, augmentation, and validation of datasets.
Performance Metrics – Knowledge of how to evaluate model efficacy using appropriate metrics.

Example questions or scenarios:

"How would you optimize a model for lower latency in production?"
"What techniques would you use for hyperparameter tuning?"

Problem-Solving Skills

Your approach to problem-solving is critical in this role. Interviewers will evaluate your ability to navigate complex challenges and provide innovative solutions. To demonstrate strength, articulate your thought process clearly and focus on structured methodologies.

Be ready to go over:

Analytical Thinking – Ability to deconstruct problems into manageable components.
Iterative Development – How you approach prototyping and refining solutions.

Example questions or scenarios:

"Describe how you would tackle a problem where the model’s predictions are inconsistent."

Collaboration and Communication

As an integral member of the Research team, your ability to work with others and communicate effectively is vital. Strong candidates demonstrate not only technical knowledge but also the ability to articulate complex ideas in understandable terms.

Be ready to go over:

Team Dynamics – How you navigate interpersonal relationships in a collaborative environment.
Feedback Loop – Your approach to integrating feedback from diverse stakeholders.

Example questions or scenarios:

"Can you provide an example of how you incorporated team feedback into your project?"

Key Responsibilities

In your role as a Research Scientist, your daily responsibilities will encompass a wide range of tasks aimed at developing innovative AI solutions. You will engage in model development and fine-tuning, ensuring that the systems you create exceed current performance benchmarks. Collaborating closely with engineering and product teams, you will translate research findings into actionable insights that inform product direction.

Primary responsibilities include:

Designing state-of-the-art AI models tailored for specific customer support tasks.
Conducting experiments with open-source models to identify performance improvements.
Breaking down complex research ideas into clear, implementable milestones.

Your role will also involve continuous collaboration with cross-functional teams to ensure that the AI systems meet the evolving needs of users and stakeholders.

Role Requirements & Qualifications

A strong candidate for the Research Scientist position will possess a mix of technical and interpersonal skills essential for success at Decagon.

Must-have skills:
- 8+ years of experience in AI/ML engineering or research.
- Proven track record in developing and deploying AI models in production.
- Experience with large-scale machine learning and fine-tuning LLMs.
Nice-to-have skills:
- Familiarity with multi-modal models.
- Experience working in a fast-paced, agile environment.
- Knowledge of specific tools and technologies relevant to Decagon's stack.

Candidates should also exhibit strong communication skills, an ability to work collaboratively, and a proactive approach to problem-solving.

Frequently Asked Questions

Q: How difficult is the interview process, and how much preparation time is typical? The interview process at Decagon is rigorous, reflecting the advanced nature of the work. Candidates typically spend several weeks preparing, focusing on both technical skills and cultural fit.

Q: What differentiates successful candidates? Successful candidates demonstrate a strong technical foundation, a collaborative spirit, and the ability to communicate complex ideas clearly. They also align well with Decagon's values of customer focus and relentless momentum.

Q: What is the culture and working style like at Decagon? Decagon fosters a culture of innovation and collaboration, with an emphasis on delivering exceptional customer experiences. Team members are encouraged to share ideas freely and work together towards common goals.

Q: What is the typical timeline from initial screen to offer? The entire process can take anywhere from a few weeks to over a month, depending on the scheduling of interviews and the specific team’s timelines.

Q: Are there remote work or hybrid expectations? Decagon operates as an in-office company, emphasizing collaboration and team dynamics. Candidates should be prepared for an in-office work environment.

Other General Tips

Align with Company Values: Show how your personal values align with Decagon's focus on customer-centricity and teamwork.
Demonstrate Technical Depth: Be prepared to dive deeply into your technical knowledge and experiences, showcasing your expertise.
Practice Clear Communication: Focus on articulating your thoughts clearly and concisely, especially when discussing complex topics.

Interview Guides

Decagon

What is a Research Scientist at Decagon?

Common Interview Questions

Technical / Domain Questions

Problem-Solving / Case Studies

Behavioral / Leadership

System Design / Architecture

Coding / Algorithms

See every interview question for this role

Practice questions from our question bank

Sign up to see all questions

Getting Ready for Your Interviews

Interview Process Overview

Deep Dive into Evaluation Areas

Technical Proficiency

Problem-Solving Skills

Collaboration and Communication

Key Responsibilities

Role Requirements & Qualifications

Frequently Asked Questions

Other General Tips

Tip

Summary & Next Steps

See every interview question for this role