Perception Engineer

Motional•Boston, MA

1d•$165,000 - $197,698•Hybrid

About The Position

Develop computer vision and machine learning solutions for autonomous vehicle perception systems, including object and signal recognition such as emergency vehicles, school buses, authority figures, and traffic-light states. Design, develop, implement, debug, and fix features in production perception software using Python and C++; integrate perception algorithms and trained machine learning models with on-car software; and architect workflows for preparing data from multiple sensors, such as cameras and LiDAR, and training machine learning models. Design evaluation processes and custom testing tools to measure prediction consistency, processing speed, and overall system safety. The role also involves formulating problems, architecting solutions and processes, training and deploying AI models, analyzing model performance, researching emerging technologies, and prototyping, evaluating, implementing, and iterating on solutions. The position is based at Motional’s Boston headquarters, with telecommuting permitted.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, or a closely related technical field.
Two years of industry experience in automotive industry software development.
Experience in production-level software development.
Experience in model deployment.
Experience with integration and validation for robotics.
Experience testing and debugging algorithms.
Experience with machine learning applications.
Experience with Python and C++.
Experience with PyTorch and GPU training.
Experience with acceleration and inference optimization.
Experience with TensorRT/CUDA.
Experience with CI/CD pipelines and Git.

Responsibilities

Develop computer vision and machine learning solutions for autonomous vehicle perception, including object and signal recognition.
Design, develop, implement, debug, and fix features in production perception software using Python and C++.
Integrate perception algorithms and trained machine learning models with on-car software.
Architect workflows to prepare data from multiple sensors, such as cameras and LiDAR, and train machine learning models.
Design evaluation processes and custom testing tools to measure prediction consistency, processing speed, and system safety.
Drive project-level impact through technology solutions.
Formulate problems, architect solutions, and design processes.
Train and deploy new AI models.
Analyze model performance and identify improvements.
Research and evaluate emerging technologies to enhance AI models.
Prototype, evaluate, implement, and iterate on solutions.