Staff Software Engineer in Test, Lidar Systems

Ouster Inc•San Francisco, CA

27d•$150,000 - $190,000

About The Position

At Ouster, we build lidar sensing technology. These sensors and accompanying software drive generational innovation cycles in industrial automation, autonomous vehicles, security, safety, crowd analytics systems, and smart cities. We have a market-leading product line of lidar sensors and are expanding our offering to include the perception software that makes 3D lidar data actionable to our customers. We believe our 3D lidar technology will serve as the foundation of tomorrow's infrastructure, improving the capabilities of nearly every device or team that depends on cameras today. We are publicly traded on the Nasdaq (Ticker: OUST), with an annual revenue run rate of >$100M, and are growing rapidly. Role Summary As a Staff Software Engineer in Test, you will be the architect and owner of the automated test framework for our cutting-edge Ouster lidar sensor software interface. You will design, build, and maintain a robust system that ensures our software is reliable, performant, and bug-free before it reaches our customers in the robotics, industrial automation, and automotive industries. While you'll be a key member of the Quality organization, you will work in close collaboration with the Design engineering team to integrate testing early and often in the development lifecycle. Your work will be critical to guaranteeing the world-class quality and reliability of our perception systems.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, or a related field.
5+ years of experience in software development or test automation.
Strong programming proficiency in Python.
Demonstrated experience building automated test frameworks using tools like Python Unittest.
Solid understanding of software QA methodologies, tools, and processes.
Experience with CI/CD systems (e.g., Jenkins, GitLab CI).
Proficient with network protocols, particularly TCP/IP and UDP.
Experience leveraging AI and LLM tools to assist in analysis and reporting.

Nice To Haves

Experience working with hardware products, sensors, robotics, or IoT devices.
Knowledge of robotics frameworks like ROS or ROS2.
Experience with Docker for creating containerized and consistent test environments.
Familiarity with data analysis and visualization libraries (e.g., Pandas, NumPy, Plotly).
Experience with performance and load testing.
Experience with reviewing and testing security and safety critical features.
Experience with testing on Windows, Linux, and MacOS operating systems.

Responsibilities

Expert Communicator: Articulate issues in a concise manner to technical and non-technical teams across the organization.
Framework Architecture & Development: Design, develop, and maintain a scalable and modular automated test framework from the ground up using modern practices in Python.
Test Case Implementation: Write clean, efficient, and maintainable automated tests that validate the functionality, performance, and stability of the lidar sensor's software interface, APIs, and communication protocols (TCP/UDP).
CI/CD Integration: Integrate the automated test suites into our CI/CD pipelines (e.g., GitLab CI, Jenkins) to enable continuous testing and provide rapid feedback to the development teams.
Hardware-in-the-Loop (HIL) Testing: Develop and manage HIL test setups to validate software behavior with physical lidar sensors, ensuring seamless hardware-software interaction.
Black-Box Type Testing: Develop and run functional, non-functional, and regression tests to verify that the hardware and software work according to the requirements and are performant, stable, and always improving.
Data Analysis & Reporting: Create tools and dashboards to automatically analyze test results, visualize sensor output data (like point clouds), and clearly communicate quality metrics to stakeholders.
Cross-Functional Collaboration: Act as the quality advocate within engineering. Work closely with software, firmware, and systems engineers to understand new features, identify potential risks, and design effective test strategies.
Root Cause Analysis: Investigate, debug, and document failures, and work with developers to ensure resolutions are timely and effective. Quickly differentiate test setup issues and deeper engineering issues.