ZipCam is making intelligent, connected dashcams for driving safety. In the U.S., more than 37,000 lives per year are lost in automobile accidents. Worldwide, an unbelievable 1.25 million people die from car crashes annually. We don't need full self-driving technology to save these lives: we can add computer vision and machine learning technology to existing cars to help people drive more safely, today, in the car they already own.
ZipCam is looking for machine learning engineers with experience in classification and object recognition in (driving) video clips. Multiple positions available.
* Summer internship. Onsite (Palo Alto) or Remote OK. Neural network analysis of driving video clips: lane-keeping, accident "near miss" detection, sign reading, stop light classification, stop line detection, other driving tasks. Also with a driver-facing camera: classification of various kinds of distraction (cell phone use, etc). Grad student or college junior/senior with with excellent course background in machine learning and computer science. Possibility to co-author an academic paper. Possible long term employment. $5k/month for a very experienced candidate.
* Midlevel or Senior Machine Learning Engineer. Full-time. Onsite (Palo Alto) or Remote OK. Major equity & good salary for the right candidate; let's talk. We are seed stage and well funded by angels. You should be have experience running accelerated ML models on video data in the cloud; otherwise we are still stack-agnostic at this point. Experience with internet-of-things (connected cameras) is a plus. Low-power (embedded) computer vision experience is a plus. Management experience is a plus; we will be hiring. Drop us a line to learn more about the product roadmap, it's exciting. This is a big moment in history for this kind of real-world machine learning.
Please send your resume + linkedin & github URLs to jobs@zip.cam. In your email please include any relevant publications. Describe some large datasets you have worked with. Looking forward to speaking with you.
ZipCam is making intelligent, connected dashcams for driving safety. In the U.S., more than 37,000 lives per year are lost in automobile accidents. Worldwide, an unbelievable 1.25 million people die from car crashes annually. We don't need full self-driving technology to save these lives: we can add computer vision and machine learning technology to existing cars to help people drive more safely, today, in the car they already own.
ZipCam is looking for machine learning engineers with experience in classification and object recognition in (driving) video clips. Multiple positions available.
* Summer internship. Onsite (Palo Alto) or Remote OK. Neural network analysis of driving video clips: lane-keeping, accident "near miss" detection, sign reading, stop light classification, stop line detection, other driving tasks. Also with a driver-facing camera: classification of various kinds of distraction (cell phone use, etc). Grad student or college junior/senior with with excellent course background in machine learning and computer science. Possibility to co-author an academic paper. Possible long term employment. $5k/month for a very experienced candidate.
* Midlevel or Senior Machine Learning Engineer. Full-time. Onsite (Palo Alto) or Remote OK. Major equity & good salary for the right candidate; let's talk. We are seed stage and well funded by angels. You should be have experience running accelerated ML models on video data in the cloud; otherwise we are still stack-agnostic at this point. Experience with internet-of-things (connected cameras) is a plus. Low-power (embedded) computer vision experience is a plus. Management experience is a plus; we will be hiring. Drop us a line to learn more about the product roadmap, it's exciting. This is a big moment in history for this kind of real-world machine learning.
Please send your resume + linkedin & github URLs to jobs@zip.cam. In your email please include any relevant publications. Describe some large datasets you have worked with. Looking forward to speaking with you.