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Industrial Placement in Data Structures and Algorithms

Make claim matching, scoring, and deduplication fast and correct.

Duration

6 months

Location

Remote

You earn

Certificate

Team

Engineering

About the role

At the heart of LitmusLayer is a verification engine that extracts factual claims from AI answers and matches them against a brand’s verified facts. That means real algorithms, including vector similarity search, token-overlap fallbacks, numerical tolerance matching, deduplication, and a composite risk-scoring formula.

As a data structures and algorithms placement student you will help make that engine faster and more correct. You will profile hot paths, tighten matching logic, add edge-case tests, and reason carefully about correctness, because in a compliance product a wrong match is a real-world risk.

What you'll work with

TypeScriptpgvectorEmbeddingsAlgorithmsVitest

What you'll do

  • Improve semantic matching using pgvector cosine similarity and its Jaccard token-overlap fallback.
  • Implement and test deduplication and merging of extracted claims.
  • Tune the composite risk-scoring formula and its edge cases.
  • Profile and optimise hot paths, and reason about time and space complexity.
  • Write thorough test cases, including the awkward inputs that break naive solutions.

What we're looking for

Preferred background: Computer Science, AI and Machine Learning, Data Science, or a related quantitative degree preferred.

  • Currently studying Computer Science, AI and Machine Learning, Data Science, or a related quantitative degree.
  • Strong foundation in data structures and algorithms, including complexity analysis.
  • Comfortable implementing and testing non-trivial logic in TypeScript or a similar language.
  • Precise thinker who cares about correctness and edge cases, not just the happy path.
  • Able to explain the trade-offs behind a chosen approach.

Nice to have

Genuinely optional. Do not let a gap here stop you applying.

  • Exposure to vector or embedding search, or information retrieval.
  • Familiarity with text processing, tokenisation, or similarity metrics.
  • Competitive-programming or algorithmic-problem-solving experience.

What you'll take away

  • How semantic search and classical fallbacks combine in a real system.
  • Practical performance profiling and complexity trade-offs on production code.
  • How algorithmic correctness maps directly onto legal and business risk.

Apply for this placement

Fill in the form and attach your résumé as a PDF. It goes straight to our team, and we read every application.

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