What you will do:

• Contribute to our data infrastructure, build new features, maintain and scale existing data processing systems
• Collaborate with machine learning scientists and infrastructure engineers to deploy state-of-the-art machine learning algorithms in production solving business problems
• Work with a variety of data processing principles: high-traffic real-time RESTful APIs, asynchronous background tasks, job scheduling workflow and more
• Improve our “Data Ops” environments by implementing unit tests, integration tests, CI/CD, and internal tools for machine learning and data pipelines

What you need to have:

• 3+ years of work experience in backend engineering (python preferred)
• Experience implementing HTTP APIs: networking, protocols, specifications
• Familiarity with the data science toolkit: python, numpy, scipy, jupyter
• Basic knowledge of data processing: arrays, vectorization, data types
• Basic knowledge of infrastructure scaling: concurrency, replication, caching, sharding
• Basic knowledge of high availability: zero downtime migrations, robustness
• Familiarity with test-driven development, git, CI/CD
• Specific interest in recommendation engines and machine learning