Who we are:
Motive empowers the people who run physical operations with tools to make their work safer, more productive, and more profitable. For the first time ever, safety, operations and finance teams can manage their drivers, vehicles, equipment, and fleet related spend in a single system. Combined with industry leading AI, the Motive platform gives you complete visibility and control, and significantly reduces manual workloads by automating and simplifying tasks.
Motive serves nearly 100,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries, including transportation and logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.
Visit gomotive.com to learn more.
About the role:
As a Computer Vision Engineer, you will be a part of a passionate team whose mission is to bring intelligence to the world’s trucks. The team is focused on building technology to understand driving behavior, identify risk factors, and intelligently suggest coachable events that not only improves the fleet safety and potentially save millions of dollars but also contribute to making the roads safer. You will have a unique opportunity to work with a high caliber and fast-paced team which consists of experienced researchers and engineers in Computer Vision, Machine Learning, Deep Learning, and Robotics with a track record of previous products and top-tier publications.
You will play a critical role in building and improving a technology that will be used by millions of trucks. In this role, you will design and implement complex computer vision systems. You will have the opportunity to build and/or improve algorithms for edge processing, implement cloud services, and integrate them to deliver end-to-end product features. You will also get exposure to large-scale ML infra and scaling that facilitates large amounts of data to train, test, and validate computer vision systems.
What You’ll Do:
- Improve the performance of our models and algorithms already in production
- Build and optimize CV/ML algorithms for real-time performance so they can run on our embedded platform i.e. the next-gen AI dashcam
- Write proficient Python and C++ code to build and improve CV algorithms, ML services, training, model compression and porting pipelines
- Convert state-of-the-art research prototypes into reliably working product features
- Collaborate with RnD team working in Computer Vision, Machine Learning, and Robotics, along with ML Platform, Embedded, Backend, and Frontend teams to deliver end-to-end product features
- Solve challenging object detection, segmentation, recognition, and geometric computer vision problems.
- Build automated deployment, validation, and active learning pipelines.
What We’re Looking For:
- Bachelors degree in computer science, software engineering, data science, artificial intelligence, or related field.
- Masters or PhD in Computer Vision, Deep Learning, and Machine Learning (preferred)
- Solid mathematical foundation in Computer Vision, Deep Learning, Machine Learning, and optimization approaches.
- Hands-on experience in training framework and tools (TensorFlow | Pytorch | Caffe)
- Strong experience in Python or C++
- Hands-on experience in building embedded algorithms and applications
- Experience in the following tools and technologies is a plus: AWS (SageMaker, Lambda, EC2, S3, RDS), CI/CD, Terraform, Docker, and Kubernetes.
- Prior experience with Eigen library and RTOS is a strong plus
Creating a diverse and inclusive workplace is one of Motive's core values. We are an equal opportunity employer and welcome people of different backgrounds, experiences, abilities and perspectives.
Please review our Candidate Privacy Notice here.
UK Candidate Privacy Notice here.
The applicant must be authorized to receive and access those commodities and technologies controlled under U.S. Export Administration Regulations. It is Motive's policy to require that employees be authorized to receive access to Motive products and technology.
