Language Engineer for Multimodal AI & Datasets

Amazon; Boston (MA); On-site; USD 75,000 - 132,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Adoption and Surrogacy Reimbursement

Job summary

Amazon’s AGI Data Services seeks Language Engineers to design and manage complex data collections spanning speech, text, and multimodal data. You will collaborate with product managers, engineers, and data scientists to generate high-quality datasets for training and evaluating AI models.

The role emphasizes building tools in Python, applying synthetic or model-based data generation, and ensuring robust annotation workflows.

Qualifications

• Experience owning language data collection projects with guidelines and annotation workflows.
• Master's degree in Computation/linguistics or equivalent required; PhD preferred.
• 2+ years in computational linguistics, language data processing, or AI data creation.
• Experience with language data annotation systems and scripting (Python).

Responsibilities

• Design complex data collections with human participants; manage planning and reporting.
• Create data using synthetic/model-based methods and assess quality targets.
• Analyze large data to extract insights and improve datasets.
• Build data analysis or creation tools using Python or similar scripting language.
• Collaborate with scientists, engineers, and data creators to evaluate AI models.

Skills

Language data collection

Python scripting

Multilingual data handling

Communication

Project coordination

Education

Master's degree in Computational Linguistics or related field

PhD in Computational Linguistics (preferred)

Tools

Python

SQL

Job description

Amazon’s AGI Data Services seeks Language Engineers to design and manage complex data collections spanning speech, text, and multimodal data. You will collaborate with product managers, engineers, and data scientists to generate high-quality datasets for training and evaluating AI models.

The role emphasizes building tools in Python, applying synthetic or model-based data generation, and ensuring robust annotation workflows.