Preetika Kaur

I am a Growth Engineer with a strong AI engineering and research background, experienced in building production-ready data pipelines, automation workflows, and experimentation systems that drive measurable product and user growth. I combine formal training in artificial intelligence and cognitive science with hands-on execution across model training, data engineering, and growth-focused tooling.

At Mercor AI, I created 100+ high-quality labeled audio-visual pairs to support multimodal model training and collaborated closely with research teams to improve data quality protocols—directly increasing the reliability of downstream model performance. In academic research at UC Berkeley, I built Python tooling for ChatGPT API modifications and conducted data analysis to surface interface friction points, supporting quality benchmarks and iterative improvement.

I have shipped end-to-end, growth-relevant projects, including:

• A Chrome extension that improves multi-tab navigation in Google Maps (user workflow optimization)

• An automated financial data pipeline for scalable data ingestion and reporting

• A visa application error-detection ML prototype with bias analysis and fairness evaluation

• A rapid Stable Diffusion optimization tool prototyped during Cal Hacks 10.0

In addition, I have hands-on experience with Clay and automation workflows, using structured data enrichment, conditional logic, and pipeline automation to support lead generation, experimentation, and operational scaling.

I bring strong programming skills (Python, C++, Java, Go), practical growth and data tooling (Pandas, Git, Excel, Clay, automation frameworks), and applied domain expertise in human–AI interaction. I am motivated to join teams that value fast iteration, rigorous evaluation, ethical AI, and shipping systems that meaningfully improve user workflows and business outcomes.