Research Scientist, Visual Data and Generative Research

Google Google · Big Tech · San Francisco, CA +2

Research Scientist focused on visual data and generative models, specifically for creating high-quality synthetic training data for foundation models. This involves designing data acquisition strategies, optimizing hardware, implementing fine-tuning methods, developing automated labeling pipelines, and creating evaluation datasets for visual quality issues.

What you'd actually do

  1. Design and execute high-throughput strategies to capture high-quality multi-view video and image data from thousands of unique participants and environments.
  2. Design and optimize specialized acquisition hardware and optical configurations to extract high-precision ground truth visual data for complex foreground subjects and environmental backgrounds.
  3. Research and implement methods to fine-tune generative video foundation models on proprietary datasets to produce high-fidelity synthetic video training data, dramatically increasing model exposure to scenes.
  4. Develop automated pipelines that generate high-resolution depth, segmentation, and motion labels from production-grade models to supervise and train next-generation research architectures.
  5. Create rigorous, large-scale image and video evaluation datasets specifically designed to measure and solve "long-tail" quality issues, such as complex material properties and temporal stability.

Skills

Required

  • Python
  • C++
  • visual data acquisition and curation for 3D vision tasks
  • designing and training neural networks
  • transformers
  • diffusion models

Nice to have

  • generative video research
  • fine-tuning or distillation of foundation vision models for novel view synthesis
  • distributed training frameworks
  • large-scale machine learning data infrastructure
  • computational photography
  • specialized sensor calibration
  • active illumination
  • multi-modal sensor fusion
  • managing end-to-end visual data pipelines

What the JD emphasized

  • PhD or equivalent practical experience in computer vision, machine learning, computer graphics, or generative media.
  • Experience designing and training neural networks, specifically with transformers or diffusion models.
  • Proven track record of managing end-to-end visual data pipelines, from initial capture strategy to automated curation and model integration.

Other signals

  • research scientist
  • generative research
  • visual data
  • foundation models
  • synthetic data