Software Engineer

Aquent is partnering with an innovative technology company at the forefront of autonomous mobility, developing advanced artificial intelligence, perception, and mapping technologies that are transforming the future of transportation. This organization combines machine learning, computer vision, robotics, and sophisticated software engineering to create systems that help autonomous vehicles safely understand and navigate complex environments. Join a collaborative engineering team where your expertise will directly contribute to advancing autonomous navigation and scalable mapping technology.

We are seeking an experienced Software Engineer, Fleet Mapping to join a Spatial AI engineering team focused on developing and automating high-definition semantic mapping capabilities. In this role, you will research, develop, train, validate, and integrate machine learning models that detect important features of the driving environment, including lanes, crosswalks, traffic lights, and other roadway elements. These models help autonomous vehicles understand their surroundings and support accurate navigation, localization, and simulation.

The primary focus of this position is automating semantic map generation and validation, reducing manual labeling, and improving the accuracy and scalability of offline mapping pipelines. The ideal candidate brings strong Python programming skills, hands-on experience developing and deploying machine learning models, and a solid understanding of deep learning frameworks and model validation. Experience with computer vision, robotics, or autonomous mapping technologies is highly valued.

What You'll Do

• 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 autonomous vehicle perception and localization.
• Design, develop, and integrate machine learning models into scalable offline mapping pipelines.
• Drive automation of new mapping features to improve mapping efficiency, accuracy, and scalability while reducing manual labeling efforts.
• 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 labeling.
• Collaborate with engineering and validation teams to establish comprehensive model testing and validation pipelines.
• Leverage and enhance existing ML infrastructure and development tools to accelerate experimentation, model training, integration, and deployment.
• Develop Python-based machine learning solutions that process sensor data and support automated mapping workflows.
• Evaluate model performance, detection accuracy, and reliability, identifying opportunities to continuously improve mapping quality.
• Collaborate with machine learning engineers, perception teams, mapping engineers, and cross-functional technical partners to deliver scalable mapping capabilities.
• Contribute to engineering best practices, technical documentation, and continuous improvements to ML development and mapping systems.

What You'll Bring

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, including experience working with cross-functional engineering and research teams.
• Ability to work independently, navigate ambiguity, and contribute effectively in a fast-paced engineering environment.

Bonus 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 ML infrastructure, scalable data pipelines, and production model deployment.
• C++ software development experience.
• Previous experience developing software for automotive, robotics, or other complex real-world systems.

Client Description:

Our client is a forward-thinking technology company developing advanced autonomous mobility solutions. The organization combines expertise in artificial intelligence, machine learning, robotics, computer vision, and large-scale software systems to create innovative technologies designed to make transportation safer, smarter, and more efficient.

Other Requirements:

• This is a six-month, full-time contract opportunity.
• The primary work arrangement is hybrid, requiring three days per week onsite in Foster City, CA.
• Exceptionally qualified remote candidates may be considered on a case-by-case basis, with preference for candidates who can collaborate within Pacific Time.
• Contract extensions may be considered based on business needs and funding but are not guaranteed.