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Machine-Learning EngineerNew York, United States


Machine-Learning Engineer

New York | United States

MapR Technologies, a provider of the industry's leading data platform for AI and Analytics, enables enterprises to inject analytics into their business processes to increase revenue, reduce costs, and mitigate risks. MapR addresses the data complexities of high-scale and mission-critical distributed processing from the cloud to the edge, IoT analytics, and container persistence. Global 2000 enterprises trust the MapR Data Platform to help them solve their most complex AI and analytics challenges. Amazon, Cisco, Google, Microsoft, SAP, and other leading businesses are all part of the MapR ecosystem. For more information, visit

Machine-Learning Engineer
Machine Learning Engineers report into MapR's Professional Services Organization and will be responsible for designing workflows and delivering solutions that process data, apply a variety of transformations and ML algorithms and then deliver the results downstream to generate value to our customers. The type of projects will vary according to data formats, use case, vertical and method of delivery. However, each project will focus on the efficient movement of data in to and out of the MapR platform and will illustrate the strengths of the environment for ML and AI. MapR's growing customer base includes most of the Fortune 50 companies which makes the work assignments challenging yet rewarding.

This role provides a significant opportunity to learn emerging ML techniques and apply big data technologies to realize opportunities that could not have been achieved just a few years ago. MapR ML Engineers report to the Director of Data Science located at company headquarters in San Jose, CA.

  • 2+ years of hands on application development experience in Java, Scala or C++
  • 2+ years experience building and implementing Apache Spark workflows
  • 3+ years of Linux & Python scripting experience
  • Experience with NoSQL (binary & json) databases
  • Experience implementing real time data feeds using Kafka API
  • Experience with Docker and ML deployment libraries and tools, such as Flask, Kubernetes, etc.
  • Strong verbal and written communication skills are required
  • Familiarity with distributed data framework 2+ years experience with open-source ML packages & environments such as Spark ML, mxnet, sklearn, TensorFlow, R, notebooks, GPUs, etc.
  • Bachelor's degree in relevant field or equivalent experience
  • Willingness to travel up to 40%

Additional requirements (preferred):
  • Ability to apply ML algorithms & model metrics to use cases based on the appropriate family of algorithms (i.e. classification, recommendation, anomaly detection, etc.)
  • Experience in a customer facing, professional services software delivery role
  • DevOps background and familiarity with visualization tools
  • Understanding of commercial IT infrastructures including storage, networking, security and systems management
  • Ability to manage professionally multiple priorities with minimal supervision and deliver on schedule
  • Work with Data Scientists to build ML Workflows that illustrate the power of the MapR platform to achieve demonstrable business value to MapR's customers
  • Demonstrate ML applications to internal sales & services teams, plus externally to potential customers
  • On-site delivery of ML applications and customization based on customer needs
  • Master the MapR Platform, including MapR-FS, MapR-DB Binary and JSON Tables, MapR-Streams and the Hadoop Eco-System products and maintain proficiency and currency as the technology evolves and advances
  • Promote MapR's ML/AI capabilities to the technical community via blogs, Hadoop User Groups (HUG's) and through participation at leading industry conferences
  • Stay current in best practices, tools, and applications used in the delivery of professional service engagements
  • Work closely with MapR sales in scoping and estimating customer professional service projects
  • Contribute to the writing of formal SOW's (Statement of Work)

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