Jobs at Pony.AI

Develop high-performance L4 autonomous driving planning algorithms and systems. Responsibilities include optimizing end-to-end model outputs with safety fallbacks, designing redundant low-compute systems, processing road-test data, and improving simulation platforms using real-world driving data. The role requires software engineering, algorithm design, problem modeling, and collaboration skills, with preferred experience in motion planning or robotics.

Responsible for designing algorithms for AI decision-making, building data pipelines, and developing evaluation metrics and testing frameworks.

The Staff/Senior Staff Software Engineer - Perception will develop advanced perception systems for autonomous vehicles, lead a team of engineers, and collaborate across functions to enhance autonomous driving technologies.

The Machine Learning Engineer will design, implement, and optimize reinforcement learning models for autonomous driving systems, enhancing data systems for scalable evaluation and performance assessment.

Assist in developing large-scale foundation models for autonomous vehicles using deep learning techniques, focusing on real-world problem-solving and model optimization.

Design and develop software architecture for self-driving vehicles, focusing on deep learning models and real-time ML solutions, while optimizing for performance and resource constraints.

Support recruiters with sourcing, screening resumes, scheduling interviews, building engineering candidate pipelines, analyzing job descriptions, assisting HR operations, and completing ad-hoc tasks.

Design and implement safe, smooth, socially aware autonomous vehicle behaviors. Convert real-world traffic scenarios into engineering frameworks and build end-to-end solutions: algorithms, evaluation tools, large-scale data pipelines, and real-time on-vehicle deployment.

Design and develop planning architectures, computational algorithms, and driving intelligence for autonomous vehicle navigation. Build scalable data processing, simulation/evaluation systems, internal tools, and collaborate cross-functionally on safety, road-rule compliance, and ride quality.