Systems Engineer - EIT

at  Kinetics Drive Solutions Inc

WHO WE ARE:

Take your career to the next step with Kinetics Drive Solutions, a leading designer and manufacturer of innovative heavy-duty drive and control solutions for land vehicles. We empower our customers globally with leading edge technologies, by pioneering intelligent solutions to realize mission-critical mobility requirements. We empower our employees to demonstrate our Right Attitudes; Own It, Integrity and Win as “A” team. If you like to be challenged, want to be energized and thrive in a fast-paced technology environment with opportunities for growth, embark on this most exciting journey with Kinetics Drive Solutions, a company of ST Engineering.

SKILLS AND COMPETENCIES:

A bachelor’s degree in engineering or a college diploma in engineering technology is required.

Understanding and ability to apply theoretical principles of:

• mechanical physical systems dynamics including rotational, translational, and vibratory motion
• fluid mechanics principles including fluid statics, fluid dynamics, and fluid motion
• electrical systems including AC/DC electrical circuits, electronic components, electro-magnetism
• Strong mechanical aptitude
• MATLAB/Simulink programming
• Self-directed problem solving (methodical and logical)
• High level of reliability and dedication, willing to work on emergencies during non-business hours as required
• Ability to work collaboratively in a team environment
• Keen attention to detail with high level of accuracy
• Excellent written and verbal communication skills
• Good organizational skills
• Attention to detail
• Ability to maintain confidentiality
• Technical writing skills
• Strong organizational skills
• Ability to travel
• Strong desire to learn and develop

THE ROLE:

This position is an interdisciplinary position that focusses on how to design, integrate, and manage complex multi discipline systems. A System Engineering EIT is responsible for requirements analysis, requirements definition, system model development, system testing and optimization. A model centric method shall be leveraged to deal with optimization methods and risk management of complex systems.