Machine Learning Engineer Intern

Key Skills

Algorithms, Computer Vision (CV), CUDA kernels, Image processing, OpenCV, TensorRT

Job Description

Metropolis is seeking a Machine Learning Engineer Intern to accelerate the development of our computer vision algorithms that would be used to power our mobility services. Reporting to the technical team lead of Machine Learning, you will be responsible for the development, deployment and ongoing optimization of the models that would be at the core of our platform. This is a challenging opportunity because the models you will build need to be optimized for different conditions like indoors and outdoors, lighting, weather and field of view. If you have a background in sensors and computer vision, and are interested in mobility, autonomous vehicles, computer vision or machine learning, this is the ideal opportunity for you.

Responsibilities

• Work with the machine learning team to train and optimize the Metropolis computer vision and machine learning algorithms.
• Collaborate with the application development team to integrate and optimize the computer vision models with the existing backend systems.
• Work with the testing engineers on software-in-the-loop, hardware-in-the-loop and field testings.

Requirements and Qualifications

• Experience on modern software design, development, version control, refactoring and testing
• Demonstrated experience implementing machine learning software, specifically computer vision detection algorithms like RCNN, SSD, YOLO, ResNet, DenseNet, etc
• Experience on deep learning framework, PyTorch/TensorFlow/JAX
• Experience with computer vision algorithms, image processing, feature extraction, tracking algorithms with OpenCV
• Experience with product/services parallel computing, accelerator architecture, CUDA, CUDNN, TensorRT libraries
• Experience with distributed/scalable systems infrastructure to operate algorithms as a software product
• Experience with large scale datasets, data pipeline, databases tools/libraries
• Excited about working in a fast-paced, dynamic startup environment.