Sr. Machine Learning Software Engineer

Sr. Machine Learning Software Engineer

Are you ready for a new challenge? We are looking for a Software Engineer to help our Machine Learning efforts to scale!

The Machine Learning Research team at Vimeo is focused on providing novel tools to Video makers and viewers. Machine Learning is used to analyze and understand content, and to allow various video Navigation experiences to our users. The team works with a wide selection of algorithms, to understand Users and Videos, based on various signals such as text, video and analytics.

In this role, you will be a key contributor in building products based on ML for Vimeo’s customers, while working in a cross-functional team and closely collaborating with researchers and data scientists!

What you’ll do:

  • Design and build MLOps systems, including data pipelines and production-level machine learning (ML) infrastructure, using tools such as Kubeflow Pipelines, Kubernetes, Apache Beam, etc. 
  • Bring ML research to production. Deploy ML models under the constraints of scalability, correctness, and maintainability, with hardware acceleration techniques. Optimize and give feedback to research-level models to bring them to production level. 
  • Use your experience and knowledge to drive software development practices in ML systems.
  • Collaborate with other agile teams of machine learning engineers, video engineers, data engineers, and others, in building machine learning infrastructure that best supports the ML needs at Vimeo.

Skills and knowledge you should possess:

  • Passionate about Data science and Machine learning products. 
  • B.Sc. or M.Sc. degree in Computer Science or a related quantitative field.
  • Proficient with cloud platforms, dockers, and Kubernetes.
  • Highly proficient in python, with ability to build APIs.
  • 4+ years of machine learning product development experience, using state-of-the-art tooling and have a deep understanding of the best practices for ML systems.
  • Experience in building high performance distributed ML systems at scale, using MLOps tools.
  • Skilled communication and a proven record of leading work across disciplines.

Bonus Points (Nice Skills to Have, but Not Needed): 

    • Experience in working with Video processing.
    • Experience with data processing and storage frameworks like Google Cloud Dataflow, Hadoop, Scalding, Spark, Storm, Cassandra, Kafka, etc. 
    • Ability to architect data pipelines using tools like Apache Beam or Spark with experience in setting best practices for data.

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