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Reuters

Large Enterprises

Reuters is an international news organization and a division of Thomson Reuters, providing global news coverage, financial data, and multimedia content to media outlets, businesses, and professionals worldwide. It is one of the world's largest and most respected news agencies, known for its wire service journalism and real-time market information.

3 interview notes · updated Jul 2026

Reuters·Software Engineer·Technical Phone Screen

Jun 2026
Python fundamentals round at Reuters for a Software Engineer position. Nothing too wild, mostly centered on tuples and how they compare to lists. Felt like a warm-up screen more than a deep technical dive.
  • What is a tuple in Python and what are its key properties, such as ordering, mutability, and hashability?
  • How does a tuple differ from a list in terms of syntax, mutability, use cases, and performance or memory?
  • Can you give concrete examples of when you'd choose a tuple over a list, such as for function return values, dictionary keys, or fixed-size records?

“Pretty standard opener.”

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Reuters·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Reuters for a software engineer role. One big open-ended question about building a web search engine at scale, the kind where you can talk for an hour and still feel like you left half the board empty.
  • Design the high-level architecture of a web search engine that can handle billions of pages and serve queries with low latency. Walk through both the offline pipeline (crawling, indexing) and the online serving path (query processing, retrieval, ranking).

“This is one of those questions where you feel good for the first ten minutes and then realize you've only covered crawling and haven't touched ranking at all.”

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Reuters·Software Engineer·Technical Phone Screen

May 2026
Theory-heavy screen for an applied scientist role at Reuters, even though the listing said software engineer. Four topics back to back, all conceptual, no coding. Felt more like a machine learning exam than a job interview.
  • Define overfitting and underfitting in supervised learning, explain how you'd detect overfitting in practice, and walk through at least three or four techniques to reduce it.
  • Explain the core idea behind contrastive learning for representation learning, including how positive and negative pairs are constructed and what kinds of downstream tasks benefit from it.
  • Describe a common contrastive loss function like InfoNCE or NT-Xent, focusing on the intuition of how it pushes representations apart or pulls them together.
  • Describe the transformer architecture at a high level, including self-attention, how it differs from RNNs or CNNs, positional encoding, and the components inside a transformer block.

“Felt confident here.”

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