Remote ML Engineer — Retrieval & Vector Search

Brilliant Systems

United States

Remote

USD 90,000 - 140,000

Full time

9 days ago

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Benefits offered by this job

Permanent, full-time role (remote/Lah0

Job summary

Brilliant Systems is seeking a mid-to-senior professional to own retrieval quality for AI systems, shaping information models before answer generation. The role spans data prep, search, ranking, evaluation, and production monitoring to drive measurable improvements.

Responsibilities include building pipelines for unstructured client data, offline evaluation, and advancing hybrid ranking methods. Remote-friendly with a base option in Lahore, compensation discussed in first conversation.

Qualifications

  • Experience with embeddings, vector search, and diagnosing retrieval failures.
  • Ability to evaluate retrieval quality and link results to production outcomes.
  • Experience applying ranking approaches to real-world data and choosing solutions based on evidence.

Responsibilities

  • Build and tune retrieval pipelines for client documents and data not originally structured for search.
  • Develop and run offline evaluations predicting production behavior.
  • Improve ranking with hybrid keyword and vector search, reranking, and simpler methods where they perform better.
  • Instrument retrieval in production to detect quality regressions early.
  • Share findings with colleagues and occasionally contribute to technical writing.

Skills

embeddings
vector search
retrieval evaluation
production metrics
ranking methods

Job description

Brilliant Systems is seeking a mid-to-senior professional to own retrieval quality for AI systems, shaping information models before answer generation. The role spans data prep, search, ranking, evaluation, and production monitoring to drive measurable improvements.

Responsibilities include building pipelines for unstructured client data, offline evaluation, and advancing hybrid ranking methods. Remote-friendly with a base option in Lahore, compensation discussed in first conversation.

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