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WyzePosted 1 month ago

Applied Scientist III - VLM R&D

$134,000–$181,000 year

HybridKirkland, Washington, United States

Full TimeDoctorate Or Professional DegreeStartup

Job Summary

Track breakthroughs in multimodal and vision-language research from academia and industry, then evaluate their relevance for in-house VLM development. Train, fine-tune, and evaluate multimodal vision-language models on large-scale, real-world home video data while designing rigorous pipelines to measure quality on tasks like event detection and temporal reasoning. Investigate user event patterns across tens of millions of authorized videos to inform model design and product direction, contributing to the architecture of a physical smart home foundation model. Build rapid proofs of concept using AI-assisted workflows to carry promising directions from idea to validated prototype, publishing research at top venues and releasing open-source models to advance the community.

Required Qualifications

  • PhD in Computer Vision, Machine Learning, or a related field
  • Master's degree with a strong track record of research or applied impact (publications, open-source contributions, or shipped ML systems)
  • Hands-on experience training and evaluating multimodal vision-language models
  • Experience in one or more of: visual transformer algorithm innovation, physical world foundation models, or embodied AI
  • Strong research sense: the ability to define the right problems, choose promising directions, and predict how research trends will translate into industry solutions
  • Proficiency with AI-assisted research and fast POC development — you use modern AI tools to multiply your own research velocity
  • Solid engineering skills in Python and deep learning frameworks (e.g., PyTorch), with the ability to work with large-scale video data pipelines

Desired Qualifications

  • Publications at top venues (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, or similar)
  • Experience with video understanding, long-context temporal modeling, or efficient inference for edge/cloud deployment
  • Experience deploying ML models in consumer products at scale

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