What does day-to-day look like: Design, develop, and maintain efficient, high-quality COBOL code to train and optimize AI models. Conduct evaluations (Evals) to benchmark model performance and analyze results for continuous improvement. Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria. Develop comprehensive explanations and rationales for evaluations, showcasing excellent reasoning and technical expertise. Lead efforts in Supervised Fine-Tuning (SFT), including creating and maintaining high-quality, task-specific datasets. Collaborate with researchers and annotators to execute Reinforcement Learning with Human Feedback (RLHF) and refine reward models.
Design innovative evaluation strategies and processes to improve the model's alignment with user needs and ethical guidelines. Create and refine optimal responses to improve AI performance, emphasizing clarity, relevance, and technical accuracy. Conduct thorough peer reviews of code and documentation, providing constructive feedback and identifying areas for improvement. Collaborate with cross-functional teams to improve model performance and contribute to product enhancements. Continuously explore and integrate new tools, techniques, and methodologies to enhance AI training processes. Requirements: Strong experience with COBOL programming language concepts.
Industry experience and knowledge of code quality, formatting, and best practices of software developmentExperience with COBOL’s testing ecosystem, including unit, integration, and property-based testing. Familiarity with COBOL frameworks and librariesKnowledge of multi-threading and asynchronous programming in COBOL. Ability to work with architectural patterns and refactor code without introducing regressions. Strong debugging skills, including fixing memory and concurrency issues. 3+ years of overall work experience with 2+ years of relevant experience with COBOLFluent in conversational and written English communication skills
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