Job Title: Prompt Engineer, Data Science & Quality Analysis

Duration: 12 Months

Location: Cupertino, CA (Hybrid - Tuesday, Wednesday, Thursday Onsite)

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

Prompt Engineering Expertise: Demonstrated ability to craft and iterate prompts for LLMs and multimodal systems to achieve targeted behaviors or outputs.
Data Analysis Skills: Experience using basic data science techniques (e.g., Python, SQL, pandas) to analyze model behavior or quality metrics.
Quality Review Mindset: Familiarity with evaluating AI-generated outputs against quality standards (e.g., accuracy, safety, diversity, bias).

Nice to haves

Prior hands-on experience in analyzing the output of large generative models like GPT-4, Gemini, or Claude.
Exposure to annotation workflows or evaluation pipelines in a research or production setting.
Background in UX research, technical writing, or human-computer interaction is a plus.
Experience working with model reviews is a bonus.

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