Diagnocat is seeking a Computer Vision Research Engineer to advance state-of-the-art AI solutions in dental imaging and treatment planning. This is a unique opportunity to apply your expertise in computer vision and deep learning to real-world healthcare challenges, pushing the boundaries of technology in the Software-as-a-Medical-Device.
About us:
Diagnocat is the global leader in AI-powered dental imaging. Our solutions help dentists reduce diagnostic errors and save valuable time by seamlessly integrating computer vision into their workflow. With CE mark in EU, FDA approval in the US, and multiple patents, our products are trusted by hundreds of clinics worldwide.
Main responsibilities:
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Research & develop state-of-the-art models for 2D/3D detection, segmentation, classification, landmarking, and object detection on X-rays, photos, CBCTs, and point clouds.
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Integrate models into production and support their performance in real-world clinical settings.
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Engineer robust pipelines for data preparation, training, and evaluation.
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Take ownership of your contributions and ensure their reliability at scale.
Requirements:
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3+ years of experience in CV with a proven track record of designing and building commercial solutions.
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Exceptional fundamentals in Machine Learning, Deep Learning, and Computer Science
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Proficiency with Python, PyTorch, NumPy, SciPy and other standard libraries used for ML development.
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A solid understanding of how DL frameworks work including memory utilization, identifying bottlenecks, optimization techniques, and leveraging features to improve training and generalization.
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Competence in Linux, git, docker, bash, and other related tools.
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Strong technical English communication skills.
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An inquisitive mindset, always eager to explore new approaches and seek improved solutions.
What we offer:
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Competitive salary with stock option opportunities.
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A chance to publish papers and patents, and contribute to state-of-the-art medical AI.
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Flexible working hours and remote work options.
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The opportunity to make a meaningful difference quickly — no endless busywork, your contributions will be deployed into production and impact real patients worldwide.
