Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands. About the Team The Vehicle Behaviors team is responsible for improving the decision-making capabilities and reliability of the AV in real world scenarios. Working broadly across domains and planning components, this team owns everything from ML-based Agent Predictions and Ego Trajectory Generation to safety-critical and heuristic-driven components like Trajectory Selection, to off-board metrics and tools used to measure performance and prevent regressions. This team has some of the most visible, high-impact work on the AV’s performance and plenty of challenging problems still to solve. About the Role We are looking for strong software engineers to help in developing and deploying motion planning components for next generation self-driving systems. This requires strong ownership over critical components that cross domain boundaries. Candidates need to understand, experiment, improve, and field state-of-the-art motion planning systems in real-time, safety-critical applications. We are focused on building a product. Candidates should have a mission-driven mindset and customer-centric obsession to deliver a compelling product, and be able to work with significant cross-functional interactions. You might be a good fit for this role if you have experience with any of the following: Classical planning techniques such as graph/tree-search, ranking, or optimization that can operate in a high-degree of uncertainty Developing cost functions or other algorithms for collision avoidance, ride quality, or adherence to traffic laws/regulations Integrating ML-based planning techniques side-by-side with heuristic approaches in production
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Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
101-250 employees