Senior Machine Learning Engineer (вакансія неактивна)

—5+ years of Python experience working in a large software system (not just web-dev)
—~3 years of experience developing distributed systems experience, ideally with some kind of architecture responsibilities, or designing component interfaces
—In the interview process, you will be evaluated on your performance in a number of coding test and design test scenarios — be prepared to think!
—Some experience with data processing:
---Pandas
---Numpy/Scipy
---Scikit-learn
---Spark
---MLlib (Spark.ml)

—Experience in some/all of these:
—Messaging like ZMQ or RabbitMQ
—API interface design and construction
—Microservice/distributed systems design and construction
—Persistent storage like Redis and MongoDB
—Parallel Computing
—Experience/understanding resource management services workflow (Hadoop/Yarn, Mesos, Kubernetes, AWS, OpenStack, Docker or any other).
—Experience working on the JVM (Java, Scala) a plus
—System/performance engineering (profiling process memory/cpu/io/network usage, system calls, flame graphs, jvm/python specific debugging instruments (pdb, visualvm, etc.))

This is an incredible opportunity to work in a company that is the embodiment of a startup culture, with horizontal organizational structure and virtually absent bureaucracy. We value initiative, creativity, and proactive mentality. This is one of the hottest tech niches, where we compete with industry juggernauts, like IBM and SAS, for domination in predictive analytics and machine learning space.

You will be exposed to cutting-edge technologies in this niche, and learn a lot about business applications of machine learning and predictive analytics, with the potential to become a subject matter expert in one of our target niches through acquired knowledge, training and direct access to our programming talent and the product itself.

As a Senior Backend developer, you turn data science techniques into scalable new features, with a focus on robust distributed architecture.
You will engage across disciplines to design and implement cutting-edge machine learning enhancements and infrastructure in DataRobot. Whether optimizing database query patterns, working on predictions tech, improving model tuning, designing storage structures necessary for new features, improving coding practices, or parallelizing code bottlenecks, you get it done while helping those you work with to be better developers.

The ideal candidate should bring new ideas from concept to implementation, write quality code, participate in design/development discussions, then translate architectural specs into working application design.