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Member Technical Staff - Image​/Video Data Engineer

Job in New York, New York County, New York, 10261, USA
Listing for: Dream3d
Full Time position
Listed on 2026-03-01
Job specializations:
  • IT/Tech
    Data Engineer, Machine Learning/ ML Engineer, AI Engineer
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below
Position: Member of the Technical Staff - Image / Video Data Engineer
Location: New York

About the Role

We are looking for an experienced engineer to lead our large-scale data processing efforts. In this role, you will be responsible for designing, building, and maintaining robust distributed systems that process terabytes of image and video data used to train state-of-the art generative models.

Key Responsibilities
  • Design, implement, and optimize complex data processing pipelines responsible for ingesting and transforming large media datasets.
  • Manage containerized applications on Kubernetes; deploy and scale distributed systems leveraging Ray to process tasks and orchestrate compute workloads.
  • Implement and deploy state-of-the-art ML models for data cleaning, processing, and preparation
  • Ensure data quality, diversity, and proper annotation (including captioning) for training readiness
  • Work closely in the model development loop to update data as necessitated by the training trajectory
Ideal Experiences
  • Deep understanding of Python and various file systems for data intensive manipulation and analysis
  • Demonstrable experience deploying, managing, and scaling containerized applications on Kubernetes clusters.
  • Hands-on experience with distributed computing engines such as Ray, including task scheduling, fault tolerance, and resource management.
  • Experience with image and video processing libraries (e.g., OpenCV, FFmpeg)
  • Experience working with large image/video datasets, including efficient data handling, transformation, and feature extraction.
  • Familiarity with data annotation and captioning processes for ML training datasets
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