Samsara Machine Learning Engineer Interview Questions + Guide in 2025

Samsara is a pioneering company in the Connected Operations™ Cloud, enabling organizations to leverage IoT data for actionable insights that enhance the safety, efficiency, and sustainability of physical operations.

The Machine Learning Engineer role at Samsara is a critical position that focuses on developing and deploying AI models for edge devices, utilizing large datasets collected from various sensors and cameras. Key responsibilities include optimizing machine learning models for real-time performance on resource-constrained devices, collaborating with firmware and hardware teams to integrate these models, and troubleshooting deployments to enhance efficiency and reliability. A successful candidate will possess strong programming skills in languages such as C++, Golang, or Python, as well as a deep understanding of machine learning frameworks like PyTorch and TensorFlow. The ideal candidate will be driven by the opportunity to make a real-world impact in industries that power the global economy, embodying Samsara's values of customer success, long-term building, and a growth mindset.

This guide is designed to provide you with the insights and knowledge necessary to excel in your interview for the Machine Learning Engineer position at Samsara, helping you to effectively communicate your skills and experiences in alignment with the company's vision and expectations.

The interview process for a Machine Learning Engineer at Samsara is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role and the company culture. The process typically unfolds as follows:

1. Initial Recruiter Call

The journey begins with a 30-minute phone call with a recruiter. This conversation serves as an introduction to Samsara and the specific role, allowing the recruiter to gauge your background, experience, and motivations. It’s also an opportunity for you to ask questions about the company and the team dynamics.

2. Technical Phone Screen

Following the initial call, candidates usually participate in a technical phone screen, which lasts about an hour. This interview focuses on coding skills and problem-solving abilities. Expect to tackle practical coding problems that may not strictly adhere to traditional LeetCode-style questions, but rather reflect real-world scenarios relevant to the role. The interview may involve using platforms like CoderPad or CodeSignal to demonstrate your coding proficiency.

3. Onsite Interviews

The onsite interview process typically consists of multiple rounds, often around four, conducted virtually. These rounds include:

• Technical Coding Round: This session focuses on your coding skills, where you may be asked to solve problems related to machine learning algorithms, data structures, or system design. The emphasis is on practical application rather than theoretical knowledge.

• System Design Round: In this round, you will be tasked with designing a system that incorporates machine learning models into edge devices. You should be prepared to discuss architecture, data flow, and integration with existing systems.

• Behavioral Interview: This session assesses your fit within the Samsara culture. Expect questions that explore your past experiences, teamwork, and how you handle challenges. The interviewers will be looking for alignment with Samsara's core values.

• Final Interview with Hiring Manager: The last round typically involves a conversation with the hiring manager, focusing on your career aspirations, alignment with the team’s goals, and any remaining questions you may have about the role or the company.

Throughout the process, candidates can expect timely communication regarding their progress and feedback from the interviewers. The overall experience is designed to be engaging and informative, reflecting the collaborative culture at Samsara.

As you prepare for your interviews, it’s essential to familiarize yourself with the types of questions that may arise in each round.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Samsara typically includes a recruiter call, a technical phone screen, and multiple rounds of onsite interviews. Familiarize yourself with this structure and prepare accordingly. Expect a mix of coding challenges, system design discussions, and behavioral assessments. Knowing what to expect can help you manage your time and energy effectively throughout the process.

Prepare for Practical Coding Challenges

Samsara's interviews tend to focus on practical coding problems rather than traditional LeetCode-style questions. Brush up on your coding skills in languages like Python, C++, or Golang, and practice implementing real-world solutions. Be ready to demonstrate your problem-solving approach and coding style, as interviewers appreciate clear and efficient code.

Emphasize Collaboration and Communication

Samsara values teamwork and collaboration, so be prepared to discuss your experiences working in teams. Highlight instances where you successfully collaborated with others to solve problems or complete projects. During the interview, communicate your thought process clearly and engage with your interviewers, as they appreciate candidates who can articulate their ideas effectively.

Showcase Your Knowledge of Machine Learning and Embedded Systems

Given the role's focus on machine learning and embedded systems, ensure you have a solid understanding of relevant concepts, frameworks, and optimization techniques. Be ready to discuss your experience with ML frameworks like TensorFlow or PyTorch, and demonstrate your ability to optimize models for edge devices. Familiarize yourself with the latest trends in computer vision and embedded AI, as this knowledge will set you apart.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit with Samsara's culture. Prepare to share stories that illustrate your problem-solving skills, adaptability, and commitment to customer success. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions.

Stay Engaged and Ask Questions

Throughout the interview process, maintain a positive and engaged demeanor. Show genuine interest in the role and the company by asking thoughtful questions about Samsara's projects, team dynamics, and future goals. This not only demonstrates your enthusiasm but also helps you gauge if the company aligns with your career aspirations.

Follow Up Professionally

After your interviews, send a thank-you email to your interviewers and the recruiter. Express your appreciation for the opportunity to interview and reiterate your interest in the position. This small gesture can leave a lasting impression and reinforce your enthusiasm for the role.

By following these tips and preparing thoroughly, you'll position yourself as a strong candidate for the Machine Learning Engineer role at Samsara. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Samsara. The interview process will likely assess your technical skills in machine learning, embedded systems, and coding, as well as your ability to collaborate and communicate effectively within a team. Be prepared to discuss your experience with real-world applications of machine learning, particularly in edge computing environments.

Machine Learning

1. Can you explain the process of deploying a machine learning model on an edge device?

Understanding the deployment process is crucial for this role, as it involves integrating models into resource-constrained environments.

How to Answer

Discuss the steps involved in model deployment, including model selection, optimization techniques, and integration with hardware. Highlight any specific experiences you have had with edge devices.

Example

“I typically start by selecting a model that meets the performance requirements for the task. I then optimize it using techniques like quantization and pruning to reduce its size and improve inference speed. Finally, I work closely with firmware teams to ensure the model integrates seamlessly with the hardware, testing it in real-world scenarios to validate its performance.”

2. What optimization techniques do you use for real-time inference on edge devices?

This question assesses your knowledge of practical optimization methods relevant to the role.

How to Answer

Mention specific techniques such as quantization, model distillation, and pruning. Provide examples of how you have applied these techniques in past projects.

Example

“I often use quantization to reduce the model size and improve inference speed without significantly sacrificing accuracy. For instance, in a recent project, I applied post-training quantization to a convolutional neural network, which allowed it to run efficiently on a low-power edge device while maintaining a high level of performance.”

Computer Vision

3. Describe your experience with real-time object detection and tracking.

Given the focus on computer vision at Samsara, this question is likely to come up.

How to Answer

Discuss specific projects where you implemented object detection and tracking algorithms, including the frameworks and tools you used.

Example

“In my previous role, I developed a real-time object detection system using YOLOv3. I integrated it with a camera feed to track vehicles in a parking lot. The system was optimized for low latency, allowing it to process frames in under 30 milliseconds, which was crucial for the application.”

4. How do you handle performance bottlenecks in edge AI deployments?

This question evaluates your troubleshooting skills and understanding of performance optimization.

How to Answer

Explain your approach to identifying and resolving bottlenecks, including profiling tools and techniques you use.

Example

“I typically start by profiling the model to identify which parts are causing delays. I use tools like TensorBoard and NVIDIA’s Nsight Systems to analyze performance metrics. Once I identify the bottlenecks, I may optimize the model architecture or adjust the input data pipeline to improve throughput.”

Coding and Algorithms

5. Can you describe a coding challenge you faced and how you solved it?

This question assesses your problem-solving skills and coding proficiency.

How to Answer

Provide a specific example of a coding challenge, detailing the problem, your approach, and the outcome.

Example

“I once faced a challenge where I needed to implement a real-time data processing pipeline for sensor data. I used Python with the Pandas library to handle data manipulation and optimized the code for performance by using vectorized operations instead of loops, which significantly reduced processing time.”

6. What is your experience with ML frameworks like TensorFlow or PyTorch?

This question gauges your familiarity with essential tools in the machine learning field.

How to Answer

Discuss your experience with these frameworks, including specific projects where you utilized them.

Example

“I have extensive experience with both TensorFlow and PyTorch. In a recent project, I used TensorFlow to build a deep learning model for image classification. I appreciated TensorFlow’s robust ecosystem for deployment, while I prefer PyTorch for its dynamic computation graph, which I find more intuitive for research purposes.”

Behavioral and Teamwork

7. How do you ensure effective collaboration with firmware and hardware teams?

This question evaluates your teamwork and communication skills.

How to Answer

Discuss your strategies for fostering collaboration, such as regular meetings, documentation, and shared goals.

Example

“I believe in maintaining open lines of communication with firmware and hardware teams. I schedule regular check-ins to discuss progress and challenges, and I ensure that all documentation is up-to-date and accessible. This collaborative approach has helped us align our goals and address issues promptly.”

8. Describe a time when you had to adapt to a significant change in a project.

This question assesses your adaptability and resilience in a fast-paced environment.

How to Answer

Provide a specific example of a project change, how you responded, and the outcome.

Example

“During a project, we had to pivot from using a traditional machine learning model to a deep learning approach due to performance issues. I quickly adapted by researching the necessary frameworks and retraining the model. This change ultimately improved our accuracy by 15%, demonstrating the importance of flexibility in our work.”