3 years of software development experience with Java
3 years of software development experience with Python
Demonstrable knowledge of computer science fundamentals, whether by a degree or otherwise
Proficient in English, both written and spoken.
Practical experience with data manipulation using Spark
Database experience (SQL or NoSQL)
Data Science experience
System/performance engineering (profiling process memory/CPU/io/network usage, system calls, flame graphs)
Publicly reviewable contributions to interesting development projects
Experience supporting user-facing code and APIs
Jenkins or other Continuous Integration platforms
AWS, Azure or GCP experience
We are looking for engineers who are willing to continuously learn, challenge themselves, and apply their knowledge to improve DataRobot’s MLOps product.
In order to keep up with the demand for new features in DataRobot, we are looking to grow our Scoring Code team. Primary responsibilities of this team include operationalizing trained machine learning models from DataRobot’s AutoML platform in Java environments. For example, embedded in other applications or scoring TBs of data with Spark.
Engineers in this team are fluent in both Java and Python.
Develop, test, operate and support features of DataRobot
Implement new machine learning models and features in Java
Create and maintain automated unit tests and functional tests
Plan capacity, manage application performance
Manage individual projects and milestones with abundant communication of progress
Seek, give, and receive critical feedback in a constructive manner, including but not limited to code review
As a member of the Inference domain, you’ll be directly responsible for the systems that enable customers to productionalize machine learning models in our MLOps product. That includes providing predictive capabilities to customers to for example assess risks of loan applications, control temperatures of manufacturing furnaces, aid in diagnosing medical conditions, help recover from current and future pandemics, and a huge number of other applications. The goal is to make predictions easier, scalable, more widely available, and, of course, trustworthy.