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Machine Learning Engineer

Industry: Construction
Remote
Company size: 11-50
Full-time

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Job description

A well-established fast-growing company using modern robotics analysis and systems to help further the construction industry is looking for a Machine Learning Engineer. The engineer will be collaborating on large, complex projects in a team, providing technical reviews and solving problems efficiently. The company does extensive site surveys on-demand or at regular intervals using a modern robot or terrestrial scanners. During the Seed round, it raised €2.5mn+ in funding. The selected candidate will actively contribute to key company projects involving Deep Learning, Machine Learning, Computer Vision, and many more.

 

Job Responsibilities:

  • Develop back-end infrastructure, data pipelines, and ML models for AI-backed services
  • Work on building ranking models and automate modeling pipelines
  • Deploy new features for solving complex data management problems
  • Integrate machine learning models for end-users and run experiments
  • Build great ML models using computer science fundamentals like data structures, algorithms, programming languages, distributed systems, and information retrieval

Job Requirements:

  • Masters/Ph.D in Computer Science, Mathematics, Applied Statistics, Physics or related fields
  • At least 3+ years of relevant experience as a Machine Learning expert
  • Well versed in working with complex ML and Data pipelines and maintenance Large Datasets
  • Thorough knowledge of technologies like Deep Learning, Machine Learning, and Computer Vision 
  • Must possess strong development skills using C++ and Python
  • Familiarity with commonly used ML software libraries like Tensorflow, Keras, etc 
  • Knowledge building and deploying data pipelines and services in AWS will be beneficial
  • A background in Deep Learning and 3D Vision is highly preferred
  • Well-versed with Graph Neural Networks
  • Deep understanding of Computer Vision, non-linear optimization, and Bayesian Statistics
  • Publication in top tier conferences NIPS, ICML, ICRL, CVPR, ICCV, or ECCV would be advantageous

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