Staff Data & ML Infra Engineer β€” Scale Video Pipelines in London

Staff Data & ML Infra Engineer β€” Scale Video Pipelines in London

London Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
Cantina Labs

At a Glance

  • Tasks: Build and scale data pipelines for large video generation models.
  • Company: Join Cantina Labs, a leader in innovative tech solutions.
  • Benefits: Enjoy competitive pay, flexible work options, and growth opportunities.
  • Other info: Collaborative environment with a focus on innovation and career advancement.
  • Why this job: Make a real impact on cutting-edge video technology and model quality.
  • Qualifications: Experience in data engineering and familiarity with Kubernetes.

The predicted salary is between 63000 - 77000 Β£ per year.

Cantina Labs is hiring a Member of Technical Staff to build and scale data pipelines for our large video generation models.

You will own annotation workflows, dataset curation, and preprocessing tools that directly improve model quality.

You’ll collaborate with research and engineering to turn experiments into scalable systems, ensuring data is clean, ready for training, and delivered efficiently within our Kubernetes-based infrastructure.

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Staff Data & ML Infra Engineer β€” Scale Video Pipelines in London employer: Cantina Labs

Cantina Labs is an exceptional employer that fosters a vibrant work culture focused on innovation and creativity in the realm of social AI. With generous benefits including competitive salaries, extensive paid time off, and comprehensive health coverage, employees are empowered to thrive both personally and professionally. The collaborative environment encourages growth and development, making it an ideal place for those passionate about shaping the future of AI and storytelling.

Cantina Labs

Contact Details:

Cantina Labs Recruitment Team

We think you need these skills to ace Staff Data & ML Infra Engineer β€” Scale Video Pipelines in London

Data Pipeline Development
Video Generation Models
Annotation Workflows
Dataset Curation
Preprocessing Tools
Model Quality Improvement
Collaboration with Research and Engineering