Applied Computer Vision Engineer

Job Description

The Video Perception (ViPER) team is responsible for developing cutting-edge solutions for Advanced Driver Assistance Systems (ADAS). Our team is responsible for developing end-to-end solutions by leveraging the latest advancements in the field of computer vision/deep learning. Our goal is to drive the future of autonomous driving systems by providing reliable, high-performance video perception systems. As part of the ViPER team, your role would entail helping us in developing L3/L4 ADAS, which would primarily involve (1) Understanding of the underlying data, derive insights/patterns/failures (2) Based on the underlying unstructured unsupervised data, come up with methods for enabling efficient searching of data (3) Derive experiments to develop perception models using this data, introducing cycles of experiments, analysis & validation Requirements Strong technical background. At least a Bachelors/Master of Engineering in Computer Science or Electrical Sciences or related areas with 4 years of industry experience in designing and implementing perception algorithms Skilled software engineer with experience in C++ and strong problem-solving skills Passionate about solving real-world robotics problems: ideally worked on autonomous driving systems before. Prior knowledge of working with deep neural networks is a strong plus. A team player: You take ownership and work with the team to deliver exceptional results Ability to build and iterate quickly (AGILE mindset). You enjoy working fast and smart, and you are comfortable in the earlier stages of developing an algorithm from scratch Good hands on: You dig deep into important details such as the sensor driver if it improves the overall system. You like working with production machine learning pipelines, from dataset collection and labeling to training and validation Great communicator: You have experience writing clear, concise, and detailed documentation. You should be able to explain technical solutions clearly to non-technical/different audience Skills Strong knowledge in DL (3yrs min) : few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition Hands on experience in Full stack Vision/Image related projects in production (3yrs min) Data: Spark, Hadoop (user level), visualization, data preparation, data handling, understanding data Model: Tensorflow 2+ & PyTorch Deployment: Docker, Flask, Kubernetes Cloud deployment: Azure/AWS Other skiils: Git, Linux, Cuda, OpenMP, Distributed systems