Autonomy Systems Software Engineer

Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.

We are looking for an Autonomy Software Systems Engineer who can derive requirements from first-principles for safe driving behavior for a self-driving vehicle. An ideal candidate will have background developing algorithms commonly used in autonomy or robotics applications in aerospace, automotive, or other domains.  This will be used to support our safety claims about safe perception, motion planning, and control for autonomous robots so that we can ethically and safely deploy autonomous trucks to save lives and transform the freight industry.

In this role, you will:

• Collaborate closely with Autonomy Software teams by using first-principles analysis to develop requirements for safe perception, localization, prediction, motion planning, and/or control algorithms.
• Identify edge cases that could help us expose weaknesses in our system prior to encountering them in on-road environments.
• Provide analysis to support complex autonomy system design trade-offs to inform system design decisions affecting safety and performance.
• Support development of simulation scenarios, structured track testing, on-road road testing, and hardware-in-the-loop testing.

What you’ll bring:

• Deep understanding of kinematics, dynamics, and system modeling
• Strong programming skills in Python, MATLAB, C++, or C
• Sensor fusion and object tracking (e.g., Kalman filtering, particle filters, least squares estimation)
• Machine learning methods (e.g., neural networks, SVMs, kNN, regression models, decision trees)
• Classical computer vision (e.g., edge/corner detection, camera calibration, optics)
• Motion planning (e.g., A*, RRT, potential fields)
• Control systems (e.g., PID, nonlinear control, model predictive control)
• Strong foundation in probability and statistics, including appropriate application of distributions (e.g., Gaussian, Poisson, binomial)
• Working knowledge of autonomy system architectures and system-level design
• Bayesian inference (e.g., beta and gamma distributions)
• Markov models and probabilistic system modeling
• Simulation development and validation environments
• System reliability analysis
• Exposure to safety-critical stand