Generative AI Data Engineer for Research Pipelines

United States Digital Space LLC; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation

European offices & remote in Europe

25 days leave + holidays

Culture & socials

Job summary

United States Digital Space LLC is seeking a data-driven ML professional to join our Data team. You will manage data lifecycles for researchers, working with over a million hours of video and audio data to build a human-centric data lake powering model training.

This role sits at the intersection of applied research, data engineering, and ML infrastructure, focusing on data quality and feature extraction to elevate model performance. Opportunities to influence long-term data strategy exist.

Qualifications

• A strong background in data-centric, applied Machine Learning, with hands-on experience improving model performance through data quality, curation, labeling, and evaluation rather than model architecture alone
• Experience working on the data layer of Generative AI products, particularly involving images, video, or audio
• Excellent Python skills, with a strong focus on writing clean, maintainable, and well-tested code
• Nice to have if you have experience designing, building, and operating workflow orchestration systems and large-scale data processing pipelines

Responsibilities

• Build and manage the world’s best human-centric data lake by collaborating with model teams
• Extract new features and annotations to elevate datasets used for training
• Improve data quality, labeling, and curation to boost model performance
• Help design, improve, and operate data processing pipelines and infrastructure

Skills

Applied ML

Generative AI data layer

Python

Workflow orchestration

Data pipelines

Job description

United States Digital Space LLC is seeking a data-driven ML professional to join our Data team. You will manage data lifecycles for researchers, working with over a million hours of video and audio data to build a human-centric data lake powering model training.

This role sits at the intersection of applied research, data engineering, and ML infrastructure, focusing on data quality and feature extraction to elevate model performance. Opportunities to influence long-term data strategy exist.