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Machine Learning Software Engineer

๐Ÿ•’ 3 days ago
Machine LearningSoftware EngineeringPythonDocker

๐Ÿ“œ Description

  • Develop production deployment systems for classical and machine learning algorithms.
  • Build robust, scalable inference pipelines.
  • Maintain existing deployment systems and develop new production capabilities.
  • Learn and implement new deployment technologies and best practices.
  • Stay current with the latest MLOps and inference optimization techniques.

๐Ÿ› ๏ธ Requirements

  • B.Sc. in Computer Science, Software Engineering, or related technical field.
  • Strong problem-solving skills for complex production challenges.
  • Proficiency in Python.
  • Experience with containerization and orchestration technologies (Docker, Kubernetes) is an advantage.
  • Hands-on experience with model serving frameworks and inference optimization is an advantage.
  • Background in distributed systems is an advantage.
Full job description
Mobileye is looking for a Machine Learning Software Engineer, who will be challenged by bridging the gap between cutting-edge machine learning research and robust production deployment.
In this position, you will combine software engineering expertise with machine learning deployment knowledge, responsible for taking our algorithms and developing robust production solutions that serve them at scale.
The work at Mobileyeโ€™s algorithms department is fast-paced and requires staying ahead of the curve with the latest engineering solutions and best practices adopted across the ML community, while staying informed about emerging solutions in both computer vision and NLP domains and understanding the specific problems they address.
Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!

What will your job look like:

  • Your role will include developing production deployment systems for classical and machine learning algorithms from research and building robust, scalable inference pipelines.
  • You will develop primarily in Python and infrastructure tools (Kubernetes, Docker, etc.), taking part in both maintaining existing deployment systems and developing new production capabilities.
  • Finally, you will need to learn and implement new deployment technologies and best practices that can address emerging production challenges as they arise, while staying current with the latest MLOps and inference optimization techniques.
  • All you need is:

  • B.Sc. in Computer Science, Software Engineering, or related technical field.
  • Strong problem-solving skills and ability to tackle complex, real-world production challenges.
  • Proficiency in Python and experience with containerization and orchestration technologies (Docker, Kubernetes)- advantage.
  • Hands-on experience with model serving frameworks and inference optimization- advantage.
  • Background in distributed systems- advantage.
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