These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparation for Lambda is distinct from typical big-tech interviews. We value practical engineering skills over abstract algorithmic puzzles. You should approach your preparation with a focus on systems mastery and domain expertise.

We evaluate candidates based on the following key criteria:

Systems & Linux Fluency – You must demonstrate a deep understanding of the environment your code runs in. We evaluate your ability to navigate Linux systems, understand kernel-level interactions, and debug production issues in real-time. This is often more important than rote memorization of algorithms.

Practical Coding Proficiency – We look for clean, maintainable, and idiomatic code, with a strong preference for Python or Go. You will be judged on your ability to translate requirements into working solutions that handle edge cases and concurrency, rather than just optimizing for Big O notation on a whiteboard.

Domain Synthesis – You need to show that you understand the specific domain you are applying for, whether that is storage, security, or backend infrastructure. We assess how well you can synthesize your knowledge of distributed systems to solve architectural problems relevant to an AI cloud provider.

The interview process at Lambda is designed to be direct, practical, and thorough. It generally moves quickly, especially if you have competing timelines. The process typically begins with a recruiter screen to align on your background and interests, followed by a screen with a Hiring Manager. The Hiring Manager screen is not just a formality; expect a resume review combined with "off-hand technical jabs" to gauge your depth immediately.

Following the screens, you will move to the technical rounds. Unlike many companies that rely solely on LeetCode-style questions, Lambda often utilizes practical assessments. You should be prepared for a "Linux Technical Interview" where you may be asked to SSH into a live EC2 instance or server to debug issues, answer random system questions, or perform tasks in a real environment. This tests your hands-on ability to work with the tools we use daily.

The final stage usually involves a series of virtual onsite interviews covering architecture, object-oriented design (OOP), and behavioral alignment. These rounds focus on your ability to design scalable systems and write production-ready code. Throughout the process, interviewers will test your composure and your ability to articulate complex technical concepts clearly.

This timeline illustrates the progression from initial contact to the final technical deep dives. Use this to plan your energy; the practical Linux and OOP rounds require high focus and readiness to code in a live environment, so ensure your development environment and terminal skills are sharp before these stages.

Your interviews will focus heavily on your ability to build and maintain the infrastructure that supports AI workloads. We prioritize candidates who can demonstrate "full-stack" understanding of infrastructure—from the Linux kernel up to the application layer.

Linux & Systems Engineering

This is a critical differentiator for Lambda. You are not just writing code; you are controlling hardware. Expect this to be tested rigorously, potentially in a live environment.

Be ready to go over:

• Linux Internals: Understanding processes, threads, memory management, and file descriptors.
• Debugging & Troubleshooting: Using tools like strace, lsof, tcpdump, top, and grep to diagnose issues on a live server.
• Networking: deeply understanding TCP/IP, DNS, routing, and how data moves between data centers.
• Advanced concepts: Kernel tuning, virtualization (KVM/QEMU), and bare-metal provisioning.

Example questions or scenarios:

• "SSH into this instance. Identify why the web server is returning 500 errors and fix it."
• "How would you debug a process that is consuming 100% CPU but isn't logging anything?"
• "Explain the boot process of a Linux server from power-on to user login."

Practical Object-Oriented Design (OOP)

We assess your ability to write clean, structured code. We often prefer Python or Go. The problems are generally not trick questions but require you to implement a functional system or feature.

Be ready to go over:

• Class Design: Structuring code with proper inheritance, encapsulation, and polymorphism.
• Language Proficiency: You must know the syntax of your chosen language (Python/Go) perfectly. Mixing syntax or struggling with basic constructs is a red flag.
• Concurrency: Handling race conditions and multi-threading, especially in Go.

Example questions or scenarios:

• "Design a rate limiter class that can handle requests from multiple users."
• "Implement a file system parser that reads specific metadata from a directory structure."
• "Refactor this script into a maintainable, object-oriented application."

System Design & Architecture

For senior roles, you will be asked to design systems that operate at the scale of an AI cloud. This involves storage, compute, and security.

Be ready to go over:

• Distributed Storage: Concepts behind object storage (S3), block storage, and file systems (NFS, Lustre).
• Scalability: How to design a control plane that manages thousands of GPUs.
• Security Architecture: Threat modeling for cloud infrastructure and securing multi-tenant environments.

Example questions or scenarios:

• "Design a distributed job scheduler for AI training workloads."
• "How would you architect a high-performance storage system for petabyte-scale datasets?"
• "Design the security architecture for a multi-tenant GPU cloud."

As a Software Engineer at Lambda, your daily work revolves around building the "Superintelligence Cloud." You are responsible for the lifecycle of products that enable high-performance computing. This includes designing and implementing APIs for cloud resource management, building storage solutions that can saturate network bandwidths, and creating the frontend interfaces that customers use to deploy their models.

You will collaborate closely with cross-functional teams, including Product Management, Platform Engineering, and Hardware Engineering. Because we are a hardware-centric cloud provider, your software often interacts directly with physical constraints—thermal management, power consumption, and network topology. You will drive projects from technical RFCs (Request for Comments) through to deployment and monitoring.

For those in specific verticals like Storage or Security, you will own the vision for those domains. This might mean architecting intelligent data tiering strategies for exabyte-scale storage or developing automated threat detection systems using our own hosted LLMs. You are expected to eliminate "firefighting" by building robust, automated systems that scale.