Research Scientist II (Video)
New
P
PindropCybersecurity, computer vision
Location: US - Remote; US-RemoteFull-TimeMiddle
SalaryUS Base Pay Range $160,000 — $185,000 USD
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Job Details
- Experience
- 3+ years of professional experience in Deep Learning
- Required Skills
- PythonKerasPyTorchTensorflowDeep LearningComputer Vision
Requirements
- PhD in a quantitative field such as Computer Science, Mathematics, Engineering, or Artificial Intelligence, or equivalent industry research experience.
- 3+ years of professional experience in Deep Learning applied to computer vision, face recognition, video deepfake detection, or general machine learning.
- Strong programming proficiency in Python.
- Ability to design, develop, and maintain research packages to train and test ML models.
- Hands-on mastery of modern machine learning frameworks such as PyTorch, TensorFlow, or Keras.
- Track record of successful, timely project delivery and ability to contribute to patents and peer-reviewed publications.
- Nice to have: functional programming experience in C/C++ or working knowledge of deployment environments using Go, TF-Lite, and TF-Micro.
- Nice to have: practical experience with real-time video processing pipelines and video data acquisition and preparation.
- Nice to have: foundational knowledge of biometrics, identity authentication, fraud patterns, or consumer security concepts.
Responsibilities
- Conduct research to develop new functionalities and improve technologies for real-time video processing and meeting analytics.
- Design, implement, and evaluate deep learning algorithms related to video processing, face recognition, and video deepfake detection.
- Design, develop, and maintain internal research packages to train and test ML models with reproducibility and computational efficiency.
- Partner with research engineers and engineering teams to deploy research models and tools into production environments.
- Participate in cross-functional team meetings and contribute to regular research and code reviews.
- Publish patents and peer-reviewed papers in computer vision, audio, and speech conferences.
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