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Demystifying Interviewing for Backend Engineers @ Netflix

The Netflix TechBlog

For many roles, you will be given a choice between a take-home coding exercise or a one-hour discussion with one of the engineers from the team. The interview panel consists of two or three engineers, a hiring manager and a recruiter. The problems you are asked to solve are related to the work of the team.

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A Day in the Life of… a Software Training Specialist

Tasktop

Meet Jason Grodan, a Software Training Specialist at Tasktop! I Also like to spend a little bit of time stretching and doing light exercises, reading or playing with my daughter before I dive into some work. My role at Tasktop is a ‘Software Training Specialist’. How do you start your day before work. years and I love it.

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What Am I Working On (RDORR): SQL Server On Linux

SQL Server According to Bob

We are a bit wiser (you could take that as older) now and we don’t wait until 5 o’clock but we still try everything we can think of to exercise the code. I have 20+ years of support experience that I am able to bring directly to the designs and implementations. For example, we were designing how to capture a crash dump.

Servers 40
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Automating chaos experiments in production

The Morning Paper

This is a fascinating paper from members of Netflix’s Resilience Engineering team describing their chaos engineering initiatives: automated controlled experiments designed to verify hypotheses about how the system should behave under gray failure conditions, and to probe for and flush out any weaknesses. Safeguards.

Latency 77
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Teaching rigorous distributed systems with efficient model checking

The Morning Paper

We would like students to consider the performance characteristics of their systems, and our tests check that their designs attain reasonable run-time performance. Consider the lab exercise to implement Paxos. and used to taking small incremental steps, fail to appreciate the need for some degree of up-front design. (Of

Systems 43
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. The infrastructure should allow them to exercise their freedom as data scientists but it should provide enough guardrails and scaffolding, so they don’t have to worry about software architecture too much. It leverages elasticity of the cloud by design?—?both

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. All ML projects are software projects.

DevOps 138