Deep Learning Engineer

2 weeks ago

palo alto, United States Deep Tempo Full time

About us

Our mission at Skidaway is to return the initiative to defenders. Our vision is of Collective Defense for Cybersecurity - via deep learning. We believe we are playing a small role in defending our way of life from ever more innovative and organized attackers.   


Role Description

This is a full-time role for a Deep Learning Engineer. The role requires a strong command of machine learning algorithms, especially deep learning, and the ability to apply them to real-world business problems. We build models from the ground up, so a thorough understanding of transformer-based models and similar approaches is critical.  


Expertise in training and inference utilizing multi-GPU clusters is required. Experience in building transformer models for natural language processing is strongly preferred.  


Our approach harnesses the collective for defense; subjects like transfer learning, the ability of our models to generalize, federated learning, and differential privacy are all day-to-day concerns where you will play a leading role.  


You will be joining a small team, so your impact will be enormous. You will be on a journey with a very experienced team of successful entrepreneurs.


The role is almost entirely remote. We gather approximately monthly in Palo Alto as a team.


Qualifications


  • 3+ years of experience in data science, statistics, or a related field
  • Expertise in PyTorch, HuggingFace and related deep-learning packages
  • Experience in transformer-based models 
  • Kaggle and similar competitive experiences are preferred as they show adaptability and speed
  • OpenSource experience helpful
  • Ability to work independently and as part of a cross-functional team
  • Excellent written and verbal communication skills
  • Cyber security experience preferred 
  • Passionate about building impactful technologies and organizations
  • Master's or PhD degree in Computer Science, Statistics, Mathematics, or a related field 

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