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Luminary

Software Engineer - Applied AI

Remote/Hybrid (Remote)$170k–$225kfulltimemidAdded today

About this role

Luminary seeks a Software Engineer focused on Applied AI to enhance its AI-powered wealth transfer platform. You'll improve document extraction accuracy, build evaluation frameworks, refine agentic workflows, and design human-centered review systems for complex estate planning data.

What you'll do

  • Enhance accuracy of core document extraction and knowledge graph building from legal files
  • Design and maintain comprehensive evaluation suites to measure model performance and prevent regressions
  • Improve the shared agent harness for orchestrating agentic advisory workflows
  • Collaborate with legal experts and product teams to identify and fix failure modes
  • Build human review interfaces that explain AI suggestions and integrate user corrections
  • Stay current with AI model developments and evaluate new capabilities against production needs

What they're looking for

  • AI/ML systems design and implementation
  • Prompt engineering and LLM orchestration
  • Evaluation frameworks and testing methodologies
  • Knowledge graph design and data modeling
  • Document processing and NLP
  • Full-stack software engineering
  • Python or similar backend languages
  • System observability and debugging

Benefits

  • Remote or hybrid work with NYC office option near Bryant Park
  • Frequent production deployments with modern CI/CD practices
  • Access to cutting-edge AI tooling (Temporal, Honeycomb, Braintrust, etc.)
  • Direct customer engagement and impact on business outcomes
  • Collaboration with domain experts and subject matter specialists
  • Small, leveraged team with investment in engineering tooling
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Luminary

Luminary builds AI-powered products and is seeking software engineers who combine technical expertise with a product-focused mindset to develop and enhance these solutions. The company values collaborative engineering teams and offers opportunities for engineers to directly influence product development.

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Likely interview questions

  • Walk us through your experience building or improving AI extraction systems—what was your biggest challenge in maintaining accuracy at scale?
  • How have you designed and maintained evaluation frameworks for AI models, and how did you balance coverage with maintainability?
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