Computer Vision (Object Detection)
Tessellate Imaging is a machine learning startup helping companies accelerate into the AI era. We empower better decision-making through data. Computer vision, data science,
and deep learning are core elements of our AI solutions. We’re looking for passionate people capable of using AI modules to innovate, design, and implement solutions for never-before solved real-world problems. Our team is a tight-knit, super collaborative group that’s on a mission to create a more curious and rational world.
Design and implement Deep Learning strategies for object detection in videos with an emphasis on robustness
Contribute to maintain a scalable infrastructure for the training, benchmarking, and deployment of models
Investigate techniques for optimally detecting known objects within videos in presence of clutter and occlusions.
Collaborate across teams, working with business, product and engineering teams to deploy solutions to enterprise customers and see firsthand how a top-notch AI startup is built.
Guide the feature development process, from infancy to production.
Learn and work in all facets of product design, from interactions to information architecture, visual design, research, and more.
Must have strong programming and software engineering skills in Python
You're adept at using machine learning frameworks (such as PyTorch, TensorFlow, MXnet, etc.) to build machine learning/deep learning models and launch training experiments.
Strong experience and understanding of modifying and applying supervised/ unsupervised machine learning algorithms for object detection
You're at least a rising undergraduate senior. The more experience you have, the better.
You have a proven track record of innovation in creating novel algorithms for real world problems in deep learning and/or computer vision (either through past internships or side projects), and your portfolio shows it.
Bonus points if
Interested? Apply now!
Please include a link to your GitHub profile and portfolio of past work. Submissions without these will not be considered.