חברות הייטק בישראלBuildots*Team Lead Deep Learning Researcher 3D

*Team Lead Deep Learning Researcher 3D

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Buildots· Software Development
Tel-Avivפורסמה החודשFULL_TIME

כישורים מהמשרה

Deep learningComputer vision3D perceptionFeature matchingSfM (Structure-from-Motion)SLAMMVS (Multi-view stereo)Depth estimationPhotogrammetry pipelinePyTorch

תיאור המשרה

Description About Buildots Buildots is the world’s most advanced construction intelligence platform. By comparing schedules and BIM against site reality, its advanced AI transforms granular data into insights that unlock operational excellence. Fortune 500 contractors and owners trust Buildots, including HOCHTIEF, Bouygues, Intel, NCC and VINCI Construction. Its impact is proven at every scale, from multi-family projects to multi-gigawatt data centers. About the Role Our algorithms team is responsible for all of the deep learning and computer vision challenges the company is facing, tackling complex real-world problems that directly affect the company’s success. You’d be joining to lead a team of experienced researchers leading the research and implementation of our algorithmic engine analyzing large streams of visual data from construction sites, together with 3D models - This is still mostly a hands-on role . What you'll do: Take ownership on a major part of Buildots analysis pipeline - analyze week spots and build roadmap for a small team of senior researches Research and design deep learning solutions for different computer vision problems, using tremendous amounts of visual and 3D data. Design, research and evaluation of our production photogrammetry pipeline — feature matching, SFM and multi-view stereo Face challenges in areas of 3D Reconstruction and localization for indoor environments Own the complete research cycle, from requirements to design, implementation, and benchmarking, producing innovative solutions for core company problems Requirements: 1+ years of experience mentoring or leading a small team of DL/CV researchers. 7 years of experience researching deep learning for computer-vision problems. Deep expertise in 3D perception, specifically within feature matching, SfM, SLAM, MVS, depth estimation, or 3D reconstruction. MSc. in CS/EE/Similar departments. Experience with deep learning libraries (preferably PyTorch) Proficiency in Python. Ability to thrive in a fast-paced startup working environment. Good vibes and a sense of humor - must. Experience with 3D foundational models (VGGT/Depth Anything) or semi-dense/dense matchers (RoMA/DKM) - Advantage *To support our hiring process, we may leverage AI-powered tools for tasks such as application screening, resume analysis, and response evaluation. All decisions remain under the oversight of our recruitment team. *By submitting your application, you agree that Buildots will process your personal data in accordance with Buildots' Privacy Policy. Requirements Requirements: 1+ years of experience mentoring or leading a small team of DL/CV researchers. 7 years of experience researching deep learning for computer-vision problems. Deep expertise in 3D perception, specifically within feature matching, SfM, SLAM, MVS, depth estimation, or 3D reconstruction. MSc. in CS/EE/Similar departments. Experience with deep learning libraries (preferably PyTorch) Proficiency in Python. Ability to thrive in a fast-paced startup working environment. Good vibes and a sense of humor - must. Experience with 3D foundational models (VGGT/Depth Anything) or semi-dense/dense matchers (RoMA/DKM) - Advantage
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על Buildots

Buildots, an award-winning leader in construction technology, leverages AI and computer vision to increase efficiencies in construction, introducing a performance-driven approach to managing construction projects. Our platform automatically generates accurate, unbiased data and actionable metrics crucial for strategic decision-making. Enhancing visibility and control for site teams and management alike, Buildots sets new standards for efficiency and productivity, effectively minimizing delays and ensuring projects are completed on time and within budget.

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