ABOUT THIS FEATURED OPPORTUNITY We are seeking a Prompt Engineer with a strong foundation in data science and quality analysis to support evaluation and improvement of large language and multimodal models at a leading tech company. In this role, you will craft, refine, and test prompts to guide generative AI systems while working cross-functionally to identify issues in model behavior. You'll analyze trends across model outputs, surface quality gaps, and contribute to the development of scalable evaluation frameworks. Ideal candidates will have prior experience in prompt design, applied data science (Python, SQL, or similar), and qualitative assessment of AI responses for quality and correctness. A keen eye for patterns, rigorous attention to detail, and strong communication skills are key to success.
THE OPPORTUNITY FOR YOU Join an AI-forward organization shaping the future of human-computer interaction. You'll have the opportunity to influence how generative models behave in real-world applications, improve the reliability and safety of cutting-edge systems, and contribute to a growing knowledge base on prompt strategies and quality signals. This is a collaborative and high-impact role that bridges technology, research, and user experience.
KEY SUCCESS FACTORS - Python Development Expertise: Proven ability to build tools and web applications using Python. Comfortable with end-to-end development, including scripting, automation, and basic front-end integration.
- Prompt Engineering Expertise: Demonstrated ability to craft, test, and iterate prompts for LLMs and multimodal systems to elicit targeted behaviors or outputs.
- Data Analysis Skills: Proficient in using Python (including pandas), SQL, and basic data science techniques to analyze model behavior, trends, and quality metrics.
NICE TO HAVES - Prior hands-on experience analyzing outputs from large generative models (e.g., GPT-4, Gemini, Claude).
- Exposure to annotation workflows, data labeling, or evaluation pipelines in research or production environments.
- Background in UX research, technical writing, or human-computer interaction.
- Familiarity with model review processes and tooling.
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