Job Description About the Role prompt engineers accurate, localized, and culturally adapted data labeling and translation
Key Responsibilities
- Collaborate with data scientists, linguists, and localization experts to ensure accuracy and cultural relevance.
- Prototype and validate AI models to demonstrate initial feasibility, potential impact, and overall effectiveness.
- Design, develop, and implement prompts for data labeling and localization processes within software applications.
- Understand the current components of the software stack, use cases and problems and iterate on solutions leveraging a solid knowledge of data structures, data formats, and data modeling.
- Conduct user testing and feedback analysis to optimize prompt design for data accuracy and linguistic consistency.
- Analyze model performance using key performance indicators (KPIs) and metrics, ensuring that AI models meet customer acceptance criteria and deliver high-quality outputs.
- Communicate technical findings and solution strategies to both technical and non-technical stakeholders, including presenting model performance and actionable insights in a clear, accessible manner.
- Collaborate on data pipelines and workflows that integrate LLMs into automated systems, enhancing both the efficiency and effectiveness of data annotation tasks.
- Create guidelines and training materials for prompt usage in data labeling and localization projects.
- Stay informed on data labeling and localization industry trends and tools to enhance prompt engineering techniques.
Technical \& Required Skills
- Deep understanding of LLMs (e.g. transformer-based architectures).
- Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks.
- Strong expertise in Python for NLU, for data processing \& transformation, and for statistical analysis. Familiarity with JSON, Javascript or XML.
- Experience with popular frameworks and libraries, including TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools.
- Familiarity with APIs and platforms for working with LLMs (e.g., OpenAI, Hugging Face, etc.).
- Knowledge of localization best practices and cultural nuances for different languages and regions.
- Strong understanding of LLM evaluation metrics and the ability to assess model reliability, bias, and generalizability.
- Experience working with data pipelines, automation tools, and integrating models into production systems to ensure scalable, reliable solutions.
- A collaborative mindset with the ability to solve complex technical challenges and work independently as needed.
- Exceptional attention to detail and a commitment to delivering high-quality, reliable AI solutions.
- Appreciation for issues of Diversity, Equity, and Inclusion in AI.
Preferred Skills And Experience
- 2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles.
- Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox).
- Experience designing and automating data annotation workflows.
- Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows.
- Familiarity with cloud platforms, containerization, and model deployment.
- Knowledge of another language.
Minimum Education Requirements
- Bachelor's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Linguistics, Localization or a related field.
