Join Our Team

• Reports to: CTO 
• Revision date: June 2022 
• Classification: Full Time Position  
• Location: Remote and at NeuTigers’ discretion 
• Start date: September, 2022 

Company Overview: 

Launched in 2018, NeuTigers is driving groundbreaking Artificial Intelligence (AI) technology to the edge, improving how people and companies manage their health, operate their businesses, and protect their data, applications, and network systems. 

A spinout of Princeton University, NeuTigers is fueled by breakthrough Intellectual Property (IP) from some of the world’s leading researchers and scientists. Our patented edge AI technology stack platform is at the core of our ability to accelerate the construct of real-world problem-solving solutions and allows for broad and rapid deployment at scale across many industry domains or sectors. 

Our team is an interdisciplinary band of serial entrepreneurs who are passionate about the greater good. We embrace the notion of transforming society, catalyzed by innovative, secure, and industry-validated AI-powered applications. 

NeuTigers is an Edge-AI startup that helps transform businesses by bringing innovative and intelligent services close to data sources and point of use. Our key focus and enabling technologies are products & services that supercharge edge analytics. 

We’re developing the next generation of energy and latency efficient artificial intelligence tools that are automating a highly complex process and allow a massive compression of machine learning models while keeping its accuracy. It dramatically cut memory footprint, inference run-time, and energy consumption so that it can embed advanced intelligence across cloud, mobile devices (smart phones/watches, wearables), edge devices and IoT sensors. Therefore, decreasing bandwidth load, reducing latency, ensuring data privacy and increasing reliability. These novel technologies will unleash unseen personalized applications across many industry and consumers sectors.  

We’re productizing the technology in our core healthcare vertical. We are providing AI-augmented decision support solutions for targeted chronic and rare diseases, through remote detection and monitoring of early onset of symptoms before the crisis happens or exacerbates, that allow more proactive intervention and cost-effective care management applications 

Our technology platform is transversal across many industries, and we’re addressing specific needs for Industrial 4.0 IoT verticals such as Automotive to power the driver monitoring systems, Energy to monitor battery management systems, and Aerospace to enable predictive maintenance along the supply chain. 

Beyond increasing access to personalized intelligent services, we also preserve privacy & security. We provide an innovative proprietary technique for detecting unknown system vulnerabilities of an IoT sensors network, manage associated vulnerabilities and improve incident response when such vulnerabilities are exploited. 

NeuTigers is committed to leveraging its technology to enrich and positively impact society. We believe that AI should have meaning and purpose so that it can serve everyone. Deploying it on the edge increases access and efficiency, reduces computing costs, provides better security, and preserves privacy. 

ABOUT THE ROLE 

What You’ll Do:  

• Use AI/ML to improve our products portfolio  
• Leverage and combine data across different types to create novel learning techniques and applications 
• Processing and cleaning data (munging/wrangling) 
• Initial data investigation and exploratory data analysis (EDA) 
• Choosing one or more potential models and algorithms 

• Apply data science methods and techniques (e.g., machine learning, statistical modeling, artificial intelligence, …) 
• Measuring and improving results (validation and tuning) 
• Design and analyze A/B tests for major ML models update and app changes  
• Delivering, communicating, and/or presenting results 
• Assist the product and business teams in making data driven decision 

What You’ll Need 

• Desire to work with intelligent, ambitious, and focused individuals in a fast-paced, invention-driven, startup environment 
• Great communication skills 
• Experience in Python including libraries such as Pytorch, TensorFlow, and SciKit 
• Experience across a wide variety of deep learning techniques such as supervised learning, reinforcement learning, and GANs  

• Experience training and applying CNNs with various types of datasets 
• Experience utilizing the GPU using CUDA 
• Background in Computer Vision or Machine Learning  
• Coding knowledge and experience with several languages: C, C++, Java, 
• Experience working on a mobile app project (iOS or Android) 

• Familiar with basic mobile/web app architecture (i.e. front-end, server-side, DB, etc) 

Your values align with ours: 

• Social Conscience, Customer Empathy, Entrepreneurship, Data-driven, Effectiveness, Curiosity, Collaboration, Creativity, Leadership. 

Location: Virtual, with about 30% face to face time at the Company’s HQ in Brooklyn, NY. 

Reference: 

1. Hongxu Yin, Pavlo Molchanov, Niraj K. Jha and Jan Kautz, “Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion, ”arXiv preprint arXiv:1912.08795, 2019 

1. X. Dai, H. Yin, and N. K. Jha, “NeST: A neural network synthesis tool based on a grow-and-prune paradigm, ”IEEE Trans. on Computers, 2019 

1. H. Yin, G. Chen, Y. Li, S. Che, W. Zhang, and N. K. Jha, “Hardware-guided symbiotic training for compact, accurate, yet execution-efficient LSTM, ”arXiv preprint arXiv:1901.10997, 2019 

1. S. Hassantabar, Z. Wang, N. K. Jha, ‘’SCANN: Synthesis of Compact and Accurate Neural Networks’’, ”arXiv preprint arXiv:1904.09090, 2019 

1. X. Dai, P. Zhang, B. Wu, H. Yin, F. Sun, Y. Wang, M. Dukhan, Y. Hu, Y. Wu, Y. Jia, P. Vajda, M. Uyttendaele, N. K. Jha, ‘’ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation’’, ”arXiv preprint arXiv:1812.08934, 2018 

1. X. Dai, H. Yin, N. K. Jha, ‘’Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks‘’,  ”arXiv preprint arXiv:1905.10952, 2019