On-site
Contractual
This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models.
● Design, develop, and maintain code modules in Pascal, Delphi, or related dialects.
● Refactor legacy Pascal codebases to enhance performance, maintainability, and readability.
● Create high-quality code-plus-instruction datasets used to fine-tune conversational coding assistants.
● Ensure code samples are syntactically correct, well-commented, and self-contained.
● Write developer-friendly documentation to support model evaluation and human review.
● Evaluate LLM-generated Pascal outputs and provide constructive, structured feedback for model improvement.
● Collaborate with peers on dataset quality reviews and alignment with project guidelines.
● Follow rigorous formatting and quality control standards to ensure data integrity and value.
● Contribute to prompt design, tooling feedback, and optimization of task workflows.
● 4+ years of professional experience in Pascal or Delphi development.
● Strong understanding of procedural programming paradigms, type systems, and BEGIN…END structured blocks.
● Proven debugging, profiling, and performance tuning skills in Pascal applications.
● Solid grasp of Git, version control workflows, CI/CD processes, and testing best practices.
● Excellent written and verbal communication skills in English.
● Experience with FCL (Form Calculation Language) or Intuit’s Tax Programming System (TPS).
● Background in TurboTax workflows or other financial/tax software systems.
● Familiarity with domain-specific DSLs or experience modernizing legacy codebases.
● Exposure to AI-assisted development tools, cloud environments (AWS, GCP), or containerization (Docker, Kubernetes).
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