Sr. AI Engineer (NLP)

Group Description

At Magna, we create technology that disrupts the industry and solves big problems for consumers, our customers, and the world around us. We’re the only mobility technology company and supplier with complete expertise across the entire vehicle.

We are committed to quality and continuous improvement because our products impact millions of people every day. But we’re more than what we make. We are a group of entrepreneurial-minded people whose collective expertise gives us a competitive advantage. World Class Manufacturing is a journey and it’s our talented people who lead us on this journey.

 

Role Summary

This position entails the development of generative AI software for path planning & decision making in autonomous driving (AD) systems. The software involves generative transformer-based and algorithm-based SW using perception results from multiple Camera/Lidar, as well as other software components, such as deep learning, large language model, dataset managing, state estimation, semantic abstraction, searching, path prediction, cost optimization, decision making, and failure handling. The Senior Research Engineer will be responsible for planning, executing, and coordinating New Mobility programs in collaboration with internal and external departments, supplier companies, institutions, and academia.

Key Responsibilities

• Evaluation of technologies and product designs with compliance to scientific principles, engineering principles, company standards, customer requirements and related specifications
• Support the development of innovative components and modules from the initial concept phase through the complete development process including design engineering, validation, prototyping, testing, and evaluation of the proposed production capable process and business case
• Contribute to team effort by providing innovative ideas for products and processes and by sharing information with other team members
• Analyze engineering results and propose product changes to determine feasibility, improvement of components and systems and functional/performance specifications
• Correlate experimental data to simulation data
• Maintain innovation project schedule by monitoring project progress, co-coordinate activities, calculating time requirements, sequencing project elements, and resolving problems
• Specify requests and coordinate all sub-contracted work (i.e., S/W development, prototyping, validation, testing) and assures timely completion
• Confer with other project engineers to clarify or resolve problems and develop designs
• Maintain proper filing systems to ensure all information, (electronic or hard copy), are stored according to departments requirements

Key Responsibilities (Cont.)

• Provide engineering support to other departments within the company as directed by the department leader
• Maintain professional and technical knowledge by reviewing professional publications and establishing personal networks
• Responsible for keeping the organization abreast of developments in his/her R&D programs.
• Prepare project progress reports
• Use related computer software and equipment to perform technical analysis tasks
• Continuing education and/or training is critical as technology evolves
• Ability to travel domestic and international
• Perform other duties as required

Key Qualifications/Requirements

• Experience in research or development in a technology area related to Autonomous Driving (as per the definition in the Job Introduction), AI/ML or Robotics, or equivalent combination of education/experience.
• Master's or higher degree in Engineering including but not limited to: Computer Science, Electrical, Robotics, Aerospace, and Mechanical Engineering
• SPECIAL KNOWLEDGE / SKILLS:
• Practical experience with transformer models
• Experience with Vision/Lidar/Radar sensors
• Proficiency in programming languages such as Python, C/C++, Matlab/Simulink, etc.
• Hands-on experience with ROS/ROS2
• Hands-on experience with edge devices
• Practical experience in training and deploying deep learning models
• Exposure to any kind of Reinforcement Learning
• Exposure to any kind of Large Language Model
• Exposure to any kind of Self-Supervised or Unsupervised learning
• Exposure to any kind of searching algorithm such as A*, Dijkstra
• Exposure to any kind of object behavior prediction
• Exposure to one or more of the following: semantic segmentation, instance segmentation, panoptic segmentation, depth estimation, key point estimation, or optical flow
• Exposure to autonomous algorithms including localization, perception, deep learning, tracking, searching, control, and random v