Software Engineer

Aquent is partnering with a pioneering technology leader at the forefront of autonomous mobility to find a talented engineer who will directly shape the future of transportation. In this role, you will have a profound impact on how autonomous vehicles safely understand, navigate, and interact with the world around them. By automating high-definition semantic mapping capabilities, your work will directly accelerate the deployment of safe, scalable, and revolutionary self-driving technologies that redefine urban transit.

About the Opportunity

We are seeking a passionate and skilled engineer to join a cutting-edge Spatial AI team focused on automating semantic map generation and validation. In this collaborative and fast-paced environment, you will research, develop, train, and deploy machine learning models that detect critical roadway features like lanes, crosswalks, and traffic lights. Your efforts will reduce manual labeling and build highly scalable offline mapping pipelines, directly empowering autonomous vehicles to localize and navigate with pinpoint accuracy. If you are excited by the prospect of solving complex, real-world robotics challenges and seeing your code run on cutting-edge autonomous platforms, this is the perfect place to grow your career.

Key Responsibilities

• Research, develop, implement, and train machine learning and deep learning models to detect and classify semantic map elements used in autonomous navigation.
• Develop automated methods to identify roadway features, including lanes, crosswalks, traffic lights, and other environmental attributes.
• Contribute to the development and enhancement of high-definition semantic mapping systems that support vehicle perception and localization.
• Design, develop, and integrate machine learning models into scalable offline mapping pipelines to improve mapping efficiency and reduce manual labeling.
• Curate, organize, label, and validate datasets used for machine learning model training, testing, and evaluation.
• Develop and improve automated validation methods for map content generated through machine learning models or manual workflows.
• Collaborate with engineering and validation teams to establish comprehensive model testing and validation pipelines.
• Leverage and enhance existing machine learning infrastructure and development tools to accelerate experimentation, training, and deployment.
• Develop Python-based machine learning solutions that process sensor data and support automated workflows.
• Evaluate model performance, detection accuracy, and reliability to continuously improve mapping quality.
• Collaborate with machine learning engineers, perception teams, mapping engineers, and cross-functional technical partners to deliver scalable mapping capabilities.

Required Qualifications

• Strong hands-on Python programming experience, particularly in machine learning, data processing, or model development applications.
• Professional experience developing, training, evaluating, and deploying machine learning or deep learning models.
• Experience with machine learning frameworks and scientific computing libraries such as PyTorch, NumPy, or comparable technologies.
• Solid understanding of machine learning workflows, including dataset preparation, model training, experimentation, performance evaluation, and validation.
• Experience integrating machine learning models into software systems, automated workflows, or production pipelines.
• Strong software engineering fundamentals, including debugging, testing, performance optimization, and developing reliable, maintainable code.
• Ability to work with complex datasets and develop scalable solutions for processing, analyzing, and validating data.
• Strong analytical and problem-solving skills, with the ability to investigate technical challenges and improve model performance.
• Excellent communication and collaboration skills, with a proven track record of working effectively in cross-functional engineering and research teams.

Preferred Qualifications

• Master's degree in Computer Science, Machine Learning, Robotics, or a related technical discipline.
• Experience with computer vision, object detection, image classification, semantic segmentation, or visual feature detection.
• Experience with robotics, autonomous vehicles, autonomous navigation, or perception systems.
• Familiarity with high-definition maps, semantic mapping, or automated mapping pipelines.
• Experience with SLAM, localization, 3D geometric mapping, or spatial data processing.
• Experience processing LiDAR, point clouds, camera imagery, or other 3D sensor data.
• Hands-on experience training and deploying deep learning models for perception, detection, or classification tasks.
• Experience with automated dataset labeling, model validation, and improving detection accuracy.
• Familiarity with machine learning infrastructure, scalable data pipelines, and production model deployment.
• C++ software development experience.