Job Title: AI Data Engineering Lead
Location: UK (Remote)
Employment Type: Full-time
Department: Artificial Intelligence
Compensation: $250K – $400K

Role Summary

We’re looking for a Data Engineering Lead to architect and scale the data pipelines that power our next-generation generative video models. This role is central to our mission of training models exclusively on clean, high-quality data.

Responsibilities

• Design and lead scalable, high-throughput data pipelines optimized for multi-modal video model training.
• Build systems for data ingestion, deduplication, quality assessment, validation, filtering, and labeling to ensure only clean, high-quality data flows through the pipeline.
• Collaborate with research to define data quality benchmarks.
• Optimize end-to-end performance across distributed data processing frameworks (e.g., Apache Spark, Ray, Airflow).
• Work with infrastructure teams to scale pipelines across thousands of GPUs.
• Work directly with the leadership on the data team roadmaps.
• Manage the team of data engineers.
• Work together with filmmakers on data acquisition.

Qualifications

• Deep experience in building and scaling data infrastructure for large-scale ML systems, ideally for video or multi-modal models.
• Solid background in ML engineering, including hands-on experience in training and optimizing classifiers.
• Experience managing large-scale datasets and pipelines in production.
• Experience in managing and leading small teams of engineers.
• Expertise in Python, Spark, Airflow, or similar data frameworks.
• Understanding of modern infrastructure: Kubernetes, Terraform, object stores (e.g., S3, GCS), and distributed computing environments.
• Strong communication and leadership skills; ability to bridge the gap between engineering and research.
• Skilled at balancing rapid, iterative delivery with a focus on long-term technical vision, ensuring solutions are both pragmatic and architecturally elegant.

Nice to Haves

• Experience working on foundational model training pipelines (image, video, or language).
• Familiarity with dataset licensing, governance, and compliance workflows.
• Experience with video-specific data challenges like frame sampling, codec variability, temporal alignment, and perceptual quality scoring.