What is a Software Engineer?

At AURORA, the role of a Software Engineer is pivotal to the mission of delivering the benefits of self-driving technology safely, quickly, and broadly. Unlike typical software roles that focus solely on digital products, engineers here work at the intersection of complex hardware, real-world physics, and advanced artificial intelligence. You are building the Aurora Driver—the brain that powers autonomous trucks and passenger vehicles.

This position demands a high degree of responsibility. The code you write directly impacts the safety and reliability of vehicles moving through the physical world. Whether you are working on the Perception team interpreting sensor data, the Motion Planning team deciding the vehicle's path, or the Cloud Infrastructure team managing petabytes of simulation data, your contributions are critical. You will work in a high-stakes, technically rigorous environment where "good enough" is not an option; precision and robustness are the standards.

You will collaborate closely with hardware engineers, research scientists, and operations teams. The work ranges from low-level systems optimization and signal processing to high-level full-stack development for internal tooling. This role offers the unique opportunity to solve unsolved problems in robotics and AI, contributing to a product that is fundamentally changing the logistics and transportation industries.

Common Interview Questions

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.

Getting Ready for Your Interviews

Preparing for an interview at AURORA requires more than just brushing up on algorithms. You must demonstrate a mindset that prioritizes safety, scalability, and systems thinking. The interviewers are looking for engineers who can navigate ambiguity and deliver high-quality solutions under constraints.

Key Evaluation Criteria

Technical Fluency and Agility – You must demonstrate deep proficiency in your primary language (typically C++ or Python for core robotics, or React/Node for tools). Interviewers evaluate not just if your code works, but if it is clean, efficient, and maintainable. You should be comfortable switching contexts, such as moving from algorithmic problem-solving to discussing practical implementation details.

Systems Thinking and Design – For many roles, you will be asked to design complex systems. This measures your ability to understand how software interacts with hardware and the physical environment. You need to show you can anticipate failure modes, manage latency, and design for reliability in a distributed or robotics-centric system.

Problem Solving in Ambiguity – Autonomous driving involves solving problems that often have no textbook answer. Interviewers look for candidates who can break down vague requirements (e.g., "design a reasonable robotics system") into concrete, actionable engineering tasks. They value a structured approach where you state assumptions clearly and justify your trade-offs.

Collaboration and Communication – AURORA operates in cross-functional teams. You will be evaluated on how well you communicate technical concepts to different audiences. This includes your ability to accept feedback during a code review simulation or explain a complex technical decision to a non-expert.

Interview Process Overview

The interview process at AURORA is designed to be rigorous yet reflective of the actual work environment. While the specific steps can vary depending on the team (e.g., Core Robotics vs. Internal Tools) and location, the general flow is consistent. Candidates typically report a process that emphasizes technical competence and cultural alignment.

Expect to start with an online assessment (OA) or a technical screen. The OA often focuses on standard algorithmic problems, ranging from easy to medium difficulty. Following this, you will likely engage in a virtual onsite loop. This stage is intense and includes multiple rounds covering coding, system design, and behavioral questions. Unlike some competitors who focus purely on abstract puzzles, AURORA interviewers often introduce practical elements—candidates have reported using collaborative tools like Jamboard for live problem-solving, discussing signal processing, or even sketching system architectures.

The philosophy here is to assess your ability to think on your feet. Recent data suggests the process can sometimes feel fluid; you might face changes in scheduling or interview steps as the company iterates on its hiring practices. However, communication is generally reported as friendly and fast. The goal is to see how you handle the "chaotic" nature of a startup environment while maintaining engineering discipline.

This timeline illustrates the typical progression from application to offer. Note that the "Technical Screen" may sometimes be replaced or augmented by a take-home project or an online coding assessment depending on the specific role. Candidates should be prepared for a "Virtual Onsite" that is mentally demanding, often lasting 4–5 hours across multiple sessions.

Deep Dive into Evaluation Areas

To succeed, you must prepare for specific technical domains. Based on recent candidate experiences, AURORA tailors its questions heavily toward the specific team you are applying for.

Algorithmic Problem Solving

This is the baseline for all engineering roles. You will face coding challenges that test your grasp of data structures and algorithms.

• Why it matters: Efficiency is critical in real-time systems like autonomous vehicles.
• Evaluation: Can you write bug-free code? do you understand Time/Space complexity?
• Be ready to go over:
  • Graph traversals (BFS/DFS) – Essential for mapping and routing.
  • Dynamic Programming – Often used in optimization problems.
  • Array and String manipulation – Standard filtering and parsing tasks.
• Example questions:
  • "Solve a LeetCode Hard problem involving graph optimization."
  • "Process a stream of sensor data to filter noise (Sliding Window)."

System Design (Robotics & Infrastructure)

For mid-to-senior roles, and specifically for robotics teams, this is a make-or-break section.

• Why it matters: You are building components of a larger, safety-critical distributed system.
• Evaluation: Ability to define interfaces, handle constraints (latency, power), and ensure reliability.
• Be ready to go over:
  • Robotics Systems – Designing a control loop or a sensor data pipeline.
  • Scalability – How to handle petabytes of simulation data.
  • Trade-offs – Consistency vs. Availability in the context of a moving vehicle.
• Example scenarios:
  • "Design a reasonable robotics system for an autonomous delivery bot."
  • "Architect a system to ingest and process Lidar data in real-time."

Domain-Specific Knowledge (Hardware vs. Web)

The content here diverges based on the role. You must know which "track" you are on.

• Why it matters: A Perception Engineer needs different skills than a Full-Stack Tools Engineer.
• Evaluation: Depth of knowledge in your specific stack.
• Be ready to go over:
  • For Robotics/Hardware: CAD drawings, signal processing, circuit basics, and C++ memory management.
  • For Web/Tools: React, MongoDB, JavaScript Promises, and User Authentication (OAuth/Sessions).
• Example questions:
  • "Explain how you would handle asynchronous operations using Promises."
  • "Draw a circuit diagram for a simple sensor interface."