1. What is a Software Engineer at Arlo Technologies?

At Arlo Technologies, the role of a Software Engineer is pivotal to our mission of providing peace of mind through intelligent, cloud-connected security solutions. You are not just writing code for a web app; you are building the lifeline that connects millions of IoT devices—cameras, doorbells, and sensors—to the users who rely on them for safety. Whether you are working on the Streaming Platform, Engineering Platform, or Frontend Experience, your work directly impacts the speed, reliability, and clarity of real-time video and audio streams.

This position sits at the intersection of cloud-native architecture, embedded systems, and AI-powered analytics. You will tackle complex challenges such as managing high-throughput MQTT ecosystems handling millions of device interactions per minute, optimizing RTSP and SIP protocols for low-latency streaming, or architecting robust Angular frontends that allow users to monitor their homes seamlessly.

The engineering culture at Arlo thrives on technical depth and operational resilience. We operate in a high-stakes environment where "flaky" systems are not an option. As a Software Engineer here, you will be expected to bring structure to complexity, leveraging modern tools like AWS, Kubernetes, and AI-assisted development workflows to push the boundaries of what is possible in the world of protection technology.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparation for Arlo is different from a standard SaaS interview. Because we deal with hardware-software integration and real-time media, you need to demonstrate that you understand how code behaves in a constrained, physical world, not just in a theoretical vacuum.

Technical Versatility & Domain Knowledge We evaluate your ability to work across the stack relevant to your role. For platform roles, this means deep knowledge of Java Spring Boot, AWS, and distributed systems. For frontend roles, we look for Angular mastery and RxJS expertise. Crucially, we value candidates who understand the nuances of IoT protocols (MQTT, RTSP, SIP) and how to manage data flow between edge devices and the cloud.

Operational Excellence & Observability Arlo engineers own their code in production. Interviewers will assess your familiarity with observability tools like Datadog and your approach to "bringing order to chaos." You should be ready to discuss how you design for failure, how you instrument code for monitoring, and how you handle incident response. We look for a mindset that prioritizes stability and rapid resolution.

Problem Solving in High-Scale Environments We look for engineers who can navigate ambiguity. You will likely face questions about scaling systems to handle millions of concurrent connections or optimizing video delivery under poor network conditions. We value bias for action—the ability to make decisive technical choices and move projects forward even when requirements are evolving.

4. Interview Process Overview

The interview process at Arlo Technologies is rigorous but designed to be transparent and practical. It generally begins with a recruiter screen to align on your background and interest in the smart home security space. This is followed by a technical screen, which may involve a coding challenge or a deep-dive discussion into your specific domain expertise (such as streaming protocols or frontend architecture).

If you advance to the onsite stage (often virtual), expect a series of interviews that test both your hands-on coding skills and your system design capabilities. Unlike generic coding interviews, our technical rounds often simulate real-world Arlo scenarios—such as designing a video ingestion pipeline or debugging a race condition in a device state manager. You will also meet with cross-functional partners, such as Product Managers or QA, to evaluate your communication style and cultural fit.

We place a strong emphasis on modern engineering practices. Don't be surprised if we discuss your experience with AI-assisted coding tools (like GitHub Copilot or Cursor) or your philosophy on CI/CD pipelines. We are looking for builders who are eager to use the best tools available to accelerate development without sacrificing quality.

The timeline above illustrates the typical flow from application to offer. Use the time between the technical screen and the final loop to refresh your knowledge on system design for IoT and concurrency, as these are frequent topics during the later stages.

5. Deep Dive into Evaluation Areas

To succeed, you must demonstrate strength in specific technical areas relevant to our ecosystem. We break down our evaluation into the following key pillars.

Core Backend & Distributed Systems

For platform and backend roles, this is the most critical area. We need to know you can build systems that survive the "thundering herd" of millions of devices reconnecting simultaneously.

Be ready to go over:

• Java & Spring Boot: Deep understanding of the ecosystem, including dependency injection and memory management.
• AWS Services: Practical experience with EKS, Lambda, DynamoDB, and Aurora.
• IoT Protocols: Understanding MQTT for device communication and the trade-offs between different messaging patterns.
• Advanced concepts: Handling "backpressure" in data streams and designing idempotent APIs for device state management.

Real-Time Streaming & Media

If you are interviewing for the Streaming Platform team, your knowledge of how video moves across the internet is paramount.

Be ready to go over:

• Streaming Protocols: Deep familiarity with RTSP (Real-Time Streaming Protocol) and SIP (Session Initiation Protocol).
• Latency Optimization: Techniques for reducing lag in live video feeds.
• Media Frameworks: Working with C++ or Java to manipulate audio/video buffers.

Frontend Architecture (Angular Focus)

For frontend roles, we move beyond basic UI building. We look for architects who can manage complex state in a real-time application.

Be ready to go over:

• Angular Deep Dives: Dependency injection, lazy loading, and change detection strategies.
• RxJS: Mastering observables for handling streams of data (e.g., live camera feeds or status updates).
• Observability: Using tools like Datadog to trace frontend performance issues and LaunchDarkly for feature flagging.

Operational Maturity & AI Workflow

We are a forward-thinking team. We evaluate how you maintain the health of your systems and your adaptability to new workflows.

Be ready to go over:

• CI/CD: Experience with Harness or similar tools to automate deployments and rollbacks.
• AI Tools: We explicitly value experience with Cursor, GitHub Copilot, or ChatGPT to accelerate refactoring and testing. Be prepared to discuss how you use these tools responsibly.
• Incident Response: How you debug high-severity production issues under pressure.