Machine Learning Engineer (Java &/or Scala)

Machine Learning; Data Science; Math or Stats background; Startup exp is strongly preferred; Scala or Java-based enterprise-grade products
Full Time
Depends on Experience
Work from home not available Travel not required

Job Description

No H1B or OPT candidates please

Job overview

Our client is hiring high caliber engineers who are highly motivated to democratize data science with automation. You will work on client's proprietary core engine components: AI-powered feature engineering and AutoML. There's a lot of interesting stuff to learn and do in building out our high performance parallel distributed platform whether you're a distributed systems coder, a Machine Learning expert, or both.

  • Our AI-powered Feature Engineering discovers and evaluates millions of combinations of joins and aggregations of relational data. To explore them efficiently we built our own distributed in-memory engine on top of Apache Spark. Skewed keys, wide tables, exploding number of records after join - we have to take care of all of that.
  • Our advanced AutoML component automatically explores and evaluates state-of-the-art ML models with automated hyper-parameter optimization and model selection. Logical plan is divided into computationally-intensive jobs that are executed in parallel with strict resiliency requirements. We take advantage of industry-standard machine learning libraries, like sklearn, XGBoost, LightGBM, TensorFlow, and Pytorch.

If you're looking to build amazing products with a smart bunch of colleagues and blow the minds of customers this is the place.

Job requirements

Non-technical

  • Startup experience is nearly required. Knowing what that means and seeking it out is required. This includes being able to seek out answers and fill in the blanks, proposing new ways of approaching problems, and being able to handle all sorts of different projects.
  • You take ownership, end-to-end, of the features that are your responsibility.
  • You love to help brainstorm and solve problems faced by your colleagues.
  • You work collaboratively, and can do so in a global multi-cultural environment.
  • You love learning, and will not be afraid to jump into something with which you have no prior experience.

Technical

  • You were a key implementor coding, testing, and shipping multiple Scala or Java-based enterprise-grade products.
  • You write clean, maintainable code using the best agile software engineering practices.
  • You do not compromise on quality, and you write the tests to guarantee it.
  • You have a strong foundation in distributed computing.
    • If you've only worked on standard web dev projects you're a better fit for our web backend team. They're cool, too.
  • You're proficient in Scala, Akka, Spark, YARN, HDFS, SQL, etc.
    • A very strong Java programmer in the other technologies might work out, too.
  • Strong CS skills including such things as time / space complexity, data structures, functional programming, understanding of operating systems...
  • CS Master's or equivalent.

Preferred

  • Working knowledge of DevOps platforms such as Jenkins, Github, JIRA, etc.
  • Machine Learning learning experience.
  • Expertise in Python is a plus.

About Client

Our client is a Silicon Valley based startup focused on full-cycle Machine Learning and Data Science automation. Our platform automates the entire process of building predictive models starting from raw business data through data and feature engineering to machine learning all the way to production. We have offices in the USA, Japan, and Poland. Fortune 500 organizations around the world use client to accelerate their ML and AI projects.

Unique to the client's Platform is its AI-powered feature engineering, which eliminates the most time-consuming and labor- and skill-intensive aspects of the full data science process by discovering and evaluating millions of features derived from relational, transactional, temporal, geo-locational, or text data.

 

Dice Id : 91112574
Position Id : 6348580
Originally Posted : 3 weeks ago
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