DataRobot offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models in a fraction of the time it used to take. The technology addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. The DataRobot platform uses massively parallel processing to train and evaluate 1000's of models in R, Python, Spark MLlib, H2O and other open source libraries. It searches through millions of possible combinations of algorithms, pre-processing steps, features, transformations and tuning parameters to deliver the best models for your dataset and prediction target.

Senior Software Engineer at DataRobot

As a software engineer working on predictions at DataRobot you will be responsible for product features that are critical for our customers success. From ensuring low latency, highly available predictions in both cloud and on-premise environments, to building features to score on gigabytes of data using Hadoop and Spark, you will have the opportunity to work with leading Data Scientists and Engineers in building a cutting edge machine learning platform.

Required Skills

  • Solid programming skills in Python
  • Experience developing large, scalable systems
  • Experience designing and deploying customer facing APIs
  • Must be willing to work in a fast-paced startup environment
  • Must be a good communicator and willing to work in a collaborative environment.
  • Must be self-motivated, able to manage conflicting priorities and accountable
  • Bachelors in Computer Science, related field or equivalent demonstrable experience

Preferred Skills

  • Experience with Java (Scala, Clojure) or C/C++
  • Experience deploying predictive models in a production environment.
  • Experience in the following:
    • System/performance engineering (profiling process memory/cpu/io/network usage, system calls, flame graphs)
    • Debugging tools for python/jvm languages, e.g., pdb, visualvm, etc.
    • Microservice/distributed systems design and construction
    • Persistent storage, e.g., Redis and MongoDB
    • Resource management services workflow (Hadoop/Yarn, Mesos, Kubernetes, AWS, OpenStack, Docker, etc.).

 

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