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Blissway

Machine Learning Engineer

Denver, Colorado (Remote)$145k–$195kfulltimemidAdded 2 months ago

About this role

Blissway is seeking a Machine Learning Engineer to join their innovative team focused on enhancing toll collection and highway safety through cutting-edge technology. The role involves taking ownership of the entire machine learning pipeline, from data collection to model deployment in real-world applications, working with a wide range of computer vision tools.

What you'll do

  • Lead the end-to-end pipeline for machine learning models.
  • Conduct dataset curation, training, and deployment monitoring.
  • Implement detection and classification algorithms for real-time data.
  • Experiment with various computer vision techniques to enhance model accuracy.
  • Handle both cloud and edge computing challenges.
  • Test ideas in real-world scenarios using owned hardware.

What they're looking for

  • Proficiency in Python and TypeScript.
  • Strong software engineering fundamentals.
  • Hands-on experience with machine learning and computer vision.
  • Ability to write production-quality code.
  • Experience in model deployment and monitoring.
  • Understanding of traditional and modern computer vision techniques.
  • Adaptability in a lean team environment.
  • Detail-oriented mindset for high-stakes projects.

Benefits

  • Opportunity to make a significant impact in the field.
  • Work in a collaborative, fast-paced startup environment.
  • Access to cutting-edge technology and real-world applications.
  • Flexibility and ownership over individual projects.
  • Competitive compensation for hard work and dedication.
  • Learn and grow with a skilled and passionate team.
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Blissway

Blissway builds AI-powered toll collection and highway safety systems that process millions of images daily from roadside sensors across US highways. The company is hiring Machine Learning Engineers, Embedded Systems Engineers, Software Engineers, and Hardware Engineers to own full-stack development from sensor data and PCB design through production deployment and real-world monitoring.

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Likely interview questions

  • Walk us through a machine learning project where you took a model from training to production. What was the hardest part, and how did you handle monitoring and iteration after deployment?
  • Describe a situation where you had to choose between a classical computer vision technique and a deep learning approach. How did you make that decision, and what was the outcome?
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