Interview Guides

Samsara Machine Learning Engineer Interview Guide 2026

Samsara

Machine Learning Engineer

What is a Machine Learning Engineer at Samsara?

As a Machine Learning Engineer at Samsara, you play a pivotal role in harnessing data to drive intelligent decision-making across various applications. This position is critical as it directly impacts the efficiency and effectiveness of Samsara's products, which include advanced fleet management, IoT solutions, and environmental monitoring systems. Your work will influence real-time analytics, predictive modeling, and algorithm development, ultimately enhancing user experience and driving business growth.

This role is particularly interesting due to the scale and complexity of the challenges faced. You will engage with large datasets, develop machine learning models, and collaborate closely with cross-functional teams to solve unique problems. The impact of your contributions can be seen in the tangible improvements in operational performance for clients, making this a rewarding opportunity for those passionate about leveraging technology for better outcomes.

Common Interview Questions

In preparing for your interview, you can expect a variety of questions that reflect both the technical and behavioral aspects of the Machine Learning Engineer role. The questions presented here are drawn from 1point3acres.com and represent a range of topics you may encounter.

Technical / Domain Questions

This category tests your understanding of machine learning concepts, algorithms, and data handling techniques.

Explain the difference between supervised and unsupervised learning.
What metrics would you use to evaluate a classification model?
Describe a time when you had to deal with an imbalanced dataset. How did you handle it?
Can you explain overfitting and how to prevent it?
Discuss the trade-offs between precision and recall in a machine learning context.

System Design / Architecture

This section evaluates your ability to design scalable and efficient systems that meet business needs.

Design a machine learning pipeline for real-time predictions. What components would you include?
How would you architect a system for processing and analyzing streaming data?
Discuss the considerations you would take into account when choosing a database for a machine learning application.

Behavioral / Leadership

Behavioral questions assess your soft skills, teamwork, and alignment with Samsara's values.

Describe a challenging project you worked on. What was your role, and how did you overcome obstacles?
How do you prioritize tasks when managing multiple projects?
Can you give an example of how you have influenced a team decision?

Coding / Algorithms

You will likely be asked to demonstrate your coding skills, especially related to algorithmic thinking.

Implement an algorithm for an argument parser.
Write a function to calculate the k-nearest neighbors.
Solve a problem involving data structure manipulation (e.g., trees, graphs).

Problem-Solving / Case Studies

This category evaluates your analytical thinking and problem-solving strategies.

Given a dataset, how would you approach building a predictive model?
Discuss how you would handle missing data in your analysis.
Describe a situation where you had to make a data-driven decision without having all the information.

See every interview question for this role

Practice questions from our question bank

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

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate Cross-Validation Impact on Model Performance

Analyze how cross-validation affects the performance metrics of a regression model predicting housing prices.

Supervised Learning

Cross-Validation

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

Machine Learning

Predict Machinery Failure Under Imbalance

Build an imbalanced binary classifier to predict machinery failure 24 hours ahead using sensor, maintenance, and usage data.

Supervised Learning

Cross-Validation

Feature Engineering

Easy

NLP

Explain Context Processing in LLMs

Build a transformer-based demo that explains tokenization, embeddings, self-attention, and next-token prediction for legal and technical text.

Neural Networks

Tokenization

Language Models

Medium

Model Evaluation

Detect Leakage in Feature Engineering

Diagnose whether feature engineering leakage caused a repeat-purchase model to fall from 0.95 to 0.69 AUC after deployment.

Cross-Validation

Calibration

Feature Engineering

Easy

Model Evaluation

Evaluate Metrics for Rare Player Behavior

Choose the right metrics for a model with 0.1% positives, where accuracy is misleading and threshold selection drives business value.

Precision

Recall

F1 Score

Medium

Machine Learning

Interpret Coefficients of Linear Regression Model

Explain the significance of coefficients in a linear regression model and their impact on predictions in a business context.

Regression

Medium

NLP

Extract Resume Skills from CVs

Build a transformer-based NER pipeline to extract and normalize skills from noisy resume text with high recall on technical skills.

Text Classification

Named Entity Recognition

Language Models

Medium

Model Evaluation

Version Data and Models Reliably

Design a production versioning strategy for data and models after campaign conversion fell from 3.8% to 3.1% and calibration worsened sharply.

Accuracy

Calibration

Threshold Tuning

Easy

NLP

Explain Transformer Design for News NLP

Explain Transformer architecture and why self-attention-based models outperform RNNs for news text understanding and classification.

Neural Networks

Language Models

Deep Learning

Hard

Pipelines

Design ML Serving Platform for Vehicle GPUs

Design a concurrent ML serving pipeline to run multiple perception models on a vehicle GPU with a max latency of 30ms.

Infrastructure

Medium

Model Evaluation

Diagnose Weekend Classification Drift

Diagnose why a support ticket classifier's urgent-ticket recall drops from 88% on weekdays to 57% on weekends and propose fixes.

A/B Testing

Threshold Tuning

Diagnosis

Medium

Model Evaluation

Design a Fair Cross-Hardware Benchmark

Redesign an LLM benchmark so latency, throughput, and quality are reproducible and fairly comparable across A100, H100, TPU v5e, and MI300X.

Accuracy

Precision

Recall

Medium

Machine Learning

Long-Tail Emergency Vehicle Detection

Design a long-tail classification strategy to detect rare emergency vehicles with high recall under tight on-device latency constraints.

Supervised Learning

Bias-Variance Tradeoff

Deep Learning

+1 more

Medium

NLP

Clinical Document QA: Fine-Tune vs RAG

Compare a fine-tuned LLM and a RAG pipeline for answering clinician questions over internal clinical documents with grounded citations.

Word Embeddings

Text Classification

Language Models

Medium

Model Evaluation

Assess Offline NDCG Impact on User Reading Time

Evaluate whether a 5% increase in NDCG correlates with a rise in user reading time for a content recommendation system.

Accuracy

Precision

Recall

Hard

Machine Learning

Harmful Video Upload Detection Pipeline

Design a multimodal classifier to detect harmful uploaded videos with extreme class imbalance and strict 30s latency and safety recall targets.

Supervised Learning

Deep Learning

Feature Engineering

Medium

Model Evaluation

Evaluate Distributed Inference Scaling Metrics

Evaluate distributed inference using throughput, latency, utilization, strong/weak scaling, and Amdahl’s law, then diagnose why 64-GPU scaling is inefficient.

Accuracy

Precision

Recall

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

To prepare effectively, focus on understanding both the technical and behavioral aspects of the role. Review your past experiences and be ready to discuss how they align with Samsara's mission and values.

Role-related knowledge – You should demonstrate a strong grasp of machine learning principles, as well as familiarity with the specific technologies used at Samsara. Interviewers will evaluate your depth of knowledge and ability to apply it to real-world scenarios.

Problem-solving ability – Showcase your analytical skills by articulating your thought process when approaching challenges. Interviewers look for candidates who can break down complex problems into manageable components.

Culture fit / values – Align your responses with Samsara's core values. Emphasize collaboration, innovation, and a customer-centric mindset in your answers.

Interview Process Overview

The interview process for the Machine Learning Engineer role at Samsara typically consists of multiple stages designed to evaluate both technical expertise and cultural fit. You will begin with an initial HR screening, followed by a technical assessment that focuses on problem-solving skills relevant to the company's business logic rather than traditional coding challenges.

The virtual onsite component usually features a series of interviews, including live coding, system design discussions, and a final behavioral interview with the hiring manager. Throughout the process, expect a collaborative atmosphere where your ability to communicate effectively and demonstrate your thought process is as important as your technical skills.

This visual timeline outlines the key stages of the interview process, helping you plan your preparation effectively. Pay attention to the pacing and rigor expected at each stage, and remember that candidates may experience slight variations based on the specific team or role level.

Deep Dive into Evaluation Areas

Technical Proficiency

This area is fundamental for a Machine Learning Engineer. Your technical skills will be assessed through problem-solving exercises, coding assessments, and discussions about your past projects. Strong performance means demonstrating a solid understanding of machine learning algorithms, data manipulation, and programming languages commonly used in the field.

Machine Learning Algorithms – Be prepared to discuss various algorithms and their applications.
Programming Skills – Proficiency in languages such as Python or R is expected.
Data Handling – Understand data preprocessing, feature selection, and model evaluation techniques.

System Design

Your ability to design scalable systems will be closely evaluated. Interviewers expect you to think through the architecture of machine learning systems, considering data flow, processing, and integration with other components.

End-to-End System Design – Be ready to design a complete system that includes data collection, processing, and model deployment.
Service Selection – Discuss criteria for choosing specific technologies or services (cloud platforms, databases, etc.).

Problem-Solving Approach

Interviewers will be interested in how you tackle complex problems. Focus on articulating your thought process, assumptions, and the steps you take to arrive at a solution.

Analytical Thinking – Showcase your ability to dissect problems and identify root causes.
Creativity in Solutions – Discuss innovative approaches you’ve taken in past projects.

Collaboration and Communication

As a Machine Learning Engineer, you will often work in teams. Your ability to communicate complex ideas clearly and collaborate effectively is crucial.

Team Dynamics – Provide examples of how you have worked with cross-functional teams.
Feedback Reception – Discuss how you handle constructive criticism and use it to improve your work.

Key Responsibilities

In your role as a Machine Learning Engineer at Samsara, you will have a variety of responsibilities that contribute to product development and enhancement. Your day-to-day tasks will involve building and optimizing machine learning models, analyzing data to extract actionable insights, and collaborating with product and software engineering teams to implement solutions.

You will lead projects that explore new algorithms and techniques, contribute to the design of data pipelines, and ensure that models are scalable and maintainable. Your work will directly influence the efficiency of operational systems and the effectiveness of customer-facing applications, making your contributions vital to the success of Samsara.

Role Requirements & Qualifications

To excel in this position, strong candidates typically possess the following qualifications:

Must-have skills:
- Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
- Experience with programming languages such as Python, Java, or C++.
- Strong understanding of data structures and algorithms.
Nice-to-have skills:
- Familiarity with cloud computing platforms (e.g., AWS, Azure).
- Experience in deploying machine learning models in production environments.
- Knowledge of containerization technologies (e.g., Docker, Kubernetes).

Frequently Asked Questions

Q: What is the difficulty level of the interview process? The interview process is generally considered to be of medium difficulty, with a mix of technical and behavioral questions. Candidates should prepare for both coding challenges and discussions about their work experience.

Q: What differentiates successful candidates? Successful candidates typically demonstrate a strong technical foundation, effective problem-solving skills, and the ability to communicate complex concepts clearly. They also align well with Samsara's values and culture.

Q: How long does the interview process take? The timeline from initial screening to offer can vary, but candidates can expect it to take several weeks. Communication from the recruiter about the process is typically prompt.

Q: What is the work culture like at Samsara? Samsara fosters a collaborative and innovative environment where teamwork and continuous learning are emphasized. Employees are encouraged to share ideas and drive initiatives.

Other General Tips

Research Samsara: Familiarize yourself with the company's products and mission. Understanding the business context will help you tailor your responses.
Practice Communication: Since collaboration is key, practice articulating your thought process clearly, especially during technical discussions.
Focus on Real-World Applications: Be prepared to discuss how your technical skills have directly impacted business outcomes in your previous roles.
Prepare Questions: Have insightful questions ready for your interviewers. This shows your interest in the role and helps you assess fit.

Tip

Be mindful of the interview format. Prepare for both virtual and in-person interviews, as the expectations may differ slightly.

Summary & Next Steps

Becoming a Machine Learning Engineer at Samsara offers an exciting opportunity to work on innovative solutions that impact real-world applications. As you prepare, focus on the key areas we've discussed, such as technical proficiency, system design, and effective communication.

Remember, thorough preparation and a clear understanding of your past experiences will significantly enhance your performance during interviews. Explore additional resources and insights on Dataford to further equip yourself. Your potential to succeed in this role is immense, and with focused effort, you can make a lasting impact at Samsara.

See every interview question for this role

Samsara

Machine Learning Engineer

What is a Machine Learning Engineer at Samsara?

Common Interview Questions

Technical / Domain Questions

This category tests your understanding of machine learning concepts, algorithms, and data handling techniques.

Explain the difference between supervised and unsupervised learning.
What metrics would you use to evaluate a classification model?
Describe a time when you had to deal with an imbalanced dataset. How did you handle it?
Can you explain overfitting and how to prevent it?
Discuss the trade-offs between precision and recall in a machine learning context.

System Design / Architecture

This section evaluates your ability to design scalable and efficient systems that meet business needs.

Design a machine learning pipeline for real-time predictions. What components would you include?
How would you architect a system for processing and analyzing streaming data?
Discuss the considerations you would take into account when choosing a database for a machine learning application.

Behavioral / Leadership

Behavioral questions assess your soft skills, teamwork, and alignment with Samsara's values.

Describe a challenging project you worked on. What was your role, and how did you overcome obstacles?
How do you prioritize tasks when managing multiple projects?
Can you give an example of how you have influenced a team decision?

Coding / Algorithms

You will likely be asked to demonstrate your coding skills, especially related to algorithmic thinking.

Implement an algorithm for an argument parser.
Write a function to calculate the k-nearest neighbors.
Solve a problem involving data structure manipulation (e.g., trees, graphs).

Problem-Solving / Case Studies

This category evaluates your analytical thinking and problem-solving strategies.

Given a dataset, how would you approach building a predictive model?
Discuss how you would handle missing data in your analysis.
Describe a situation where you had to make a data-driven decision without having all the information.

See every interview question for this role

Practice questions from our question bank

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

Easy

Model Evaluation

Explain Precision vs Recall

Explain why a pneumonia classifier with 91% precision but 68% recall may still be unsafe, and recommend which metric to prioritize.

Precision

Recall

F1 Score

Easy

Model Evaluation

Interpret F1 for Imbalanced Classification

Explain why F1 is more informative than accuracy for a fraud model with 97.2% accuracy but only 18% recall on a 1% positive class.

Precision

Recall

F1 Score

Medium

Model Evaluation

Evaluate Cross-Validation Impact on Model Performance

Analyze how cross-validation affects the performance metrics of a regression model predicting housing prices.

Supervised Learning

Cross-Validation

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

Machine Learning

Predict Machinery Failure Under Imbalance

Build an imbalanced binary classifier to predict machinery failure 24 hours ahead using sensor, maintenance, and usage data.

Supervised Learning

Cross-Validation

Feature Engineering

Easy

NLP

Explain Context Processing in LLMs

Build a transformer-based demo that explains tokenization, embeddings, self-attention, and next-token prediction for legal and technical text.

Neural Networks

Tokenization

Language Models

Medium

Model Evaluation

Detect Leakage in Feature Engineering

Diagnose whether feature engineering leakage caused a repeat-purchase model to fall from 0.95 to 0.69 AUC after deployment.

Cross-Validation

Calibration

Feature Engineering

Easy

Model Evaluation

Evaluate Metrics for Rare Player Behavior

Choose the right metrics for a model with 0.1% positives, where accuracy is misleading and threshold selection drives business value.

Precision

Recall

F1 Score

Medium

Machine Learning

Interpret Coefficients of Linear Regression Model

Explain the significance of coefficients in a linear regression model and their impact on predictions in a business context.

Regression

Medium

NLP

Extract Resume Skills from CVs

Build a transformer-based NER pipeline to extract and normalize skills from noisy resume text with high recall on technical skills.

Text Classification

Named Entity Recognition

Language Models

Medium

Model Evaluation

Version Data and Models Reliably

Design a production versioning strategy for data and models after campaign conversion fell from 3.8% to 3.1% and calibration worsened sharply.

Accuracy

Calibration

Threshold Tuning

Easy

NLP

Explain Transformer Design for News NLP

Explain Transformer architecture and why self-attention-based models outperform RNNs for news text understanding and classification.

Neural Networks

Language Models

Deep Learning

Hard

Pipelines

Design ML Serving Platform for Vehicle GPUs

Design a concurrent ML serving pipeline to run multiple perception models on a vehicle GPU with a max latency of 30ms.

Infrastructure

Medium

Model Evaluation

Diagnose Weekend Classification Drift

Diagnose why a support ticket classifier's urgent-ticket recall drops from 88% on weekdays to 57% on weekends and propose fixes.

A/B Testing

Threshold Tuning

Diagnosis

Medium

Model Evaluation

Design a Fair Cross-Hardware Benchmark

Redesign an LLM benchmark so latency, throughput, and quality are reproducible and fairly comparable across A100, H100, TPU v5e, and MI300X.

Accuracy

Precision

Recall

Medium

Machine Learning

Long-Tail Emergency Vehicle Detection

Design a long-tail classification strategy to detect rare emergency vehicles with high recall under tight on-device latency constraints.

Supervised Learning

Bias-Variance Tradeoff

Deep Learning

+1 more

Medium

NLP

Clinical Document QA: Fine-Tune vs RAG

Compare a fine-tuned LLM and a RAG pipeline for answering clinician questions over internal clinical documents with grounded citations.

Word Embeddings

Text Classification

Language Models

Medium

Model Evaluation

Assess Offline NDCG Impact on User Reading Time

Evaluate whether a 5% increase in NDCG correlates with a rise in user reading time for a content recommendation system.

Accuracy

Precision

Recall

Hard

Machine Learning

Harmful Video Upload Detection Pipeline

Design a multimodal classifier to detect harmful uploaded videos with extreme class imbalance and strict 30s latency and safety recall targets.

Supervised Learning

Deep Learning

Feature Engineering

Medium

Model Evaluation

Evaluate Distributed Inference Scaling Metrics

Evaluate distributed inference using throughput, latency, utilization, strong/weak scaling, and Amdahl’s law, then diagnose why 64-GPU scaling is inefficient.

Accuracy

Precision

Recall

Sign up to see all questions

Create a free account to access every interview question for this role.

Getting Ready for Your Interviews

Culture fit / values – Align your responses with Samsara's core values. Emphasize collaboration, innovation, and a customer-centric mindset in your answers.

Interview Process Overview

Deep Dive into Evaluation Areas

Technical Proficiency

Machine Learning Algorithms – Be prepared to discuss various algorithms and their applications.
Programming Skills – Proficiency in languages such as Python or R is expected.
Data Handling – Understand data preprocessing, feature selection, and model evaluation techniques.

System Design

End-to-End System Design – Be ready to design a complete system that includes data collection, processing, and model deployment.
Service Selection – Discuss criteria for choosing specific technologies or services (cloud platforms, databases, etc.).

Problem-Solving Approach

Interviewers will be interested in how you tackle complex problems. Focus on articulating your thought process, assumptions, and the steps you take to arrive at a solution.

Analytical Thinking – Showcase your ability to dissect problems and identify root causes.
Creativity in Solutions – Discuss innovative approaches you’ve taken in past projects.

Collaboration and Communication

As a Machine Learning Engineer, you will often work in teams. Your ability to communicate complex ideas clearly and collaborate effectively is crucial.

Team Dynamics – Provide examples of how you have worked with cross-functional teams.
Feedback Reception – Discuss how you handle constructive criticism and use it to improve your work.

Key Responsibilities

Role Requirements & Qualifications

To excel in this position, strong candidates typically possess the following qualifications:

Must-have skills:
- Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
- Experience with programming languages such as Python, Java, or C++.
- Strong understanding of data structures and algorithms.
Nice-to-have skills:
- Familiarity with cloud computing platforms (e.g., AWS, Azure).
- Experience in deploying machine learning models in production environments.
- Knowledge of containerization technologies (e.g., Docker, Kubernetes).

Frequently Asked Questions

Other General Tips

Research Samsara: Familiarize yourself with the company's products and mission. Understanding the business context will help you tailor your responses.
Practice Communication: Since collaboration is key, practice articulating your thought process clearly, especially during technical discussions.
Focus on Real-World Applications: Be prepared to discuss how your technical skills have directly impacted business outcomes in your previous roles.
Prepare Questions: Have insightful questions ready for your interviewers. This shows your interest in the role and helps you assess fit.

Tip

Be mindful of the interview format. Prepare for both virtual and in-person interviews, as the expectations may differ slightly.