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Deep Learning Architect

2 months ago

Austin, Texas, United States Apera Full time
About the Role

This is a unique opportunity to join Apera, a pioneering organization building foundational AI for defense. Our first cutting-edge product is an automated targeting AI that detects, remembers, and shares targets in real-time by watching any sensor on any network. This AI delivers modular, insightful knowledge of where adversaries are on any drone, ship, vehicle, satellite, or command center. It has been tested in more than DOD exercises on sea, land, and air, and industry partnerships.

Apera has secured more than $12M in funding, making this a great time to join a highly talented team and mission leaders in defense with the opportunity to make an impact from day one.

Key Responsibilities
  • Lead the development, iterative improvement, and optimization of computer vision algorithms and models, driving the overall vision of our AI solutions.
  • Design, develop, and implement cutting-edge computer vision algorithms and models for defense applications.
  • Collaborate with software engineers, product managers, and data scientists to define and refine ML product requirements and deliverables.
  • Conduct research on the latest advancements in computer vision and machine learning, and identify potential applications.
Requirements
  • Strong proficiency in designing, training, and deploying state-of-the-art deep learning models.
  • Strong proficiency in PyTorch or other popular ML frameworks.
  • Strong proficiency in coding and data science.
  • End-to-end proficiency at the model engineering process, from data acquisition, storage, processing, and interface design, to model architecting and training, to local or cloud model deployment.
  • Proficiency in Probability Theory and other mathematical areas relevant to ML.
  • The ideal candidate will have a publication record or demonstrate the ability to make novel contributions to computer vision research.
  • Significant experience with computer vision, object detection, self-supervised learning, spatiotemporal deep learning, multimodal sensor fusion, and/or zero-shot learning.
  • Experience with robust models and out-of-distribution data at test time.
  • US Citizenship and eligibility for a Secret security clearance is required.