Chief Scientist for Sensitive Content Detection

3 days ago


Bengaluru, Karnataka, India ADCI - BLR 14 SEZ - F07 Full time

As a Senior Applied Scientist at ADCI in Bangalore, you will be part of a fast-paced organization that develops cutting-edge technologies for sensitive content detection across all languages and countries. Our team aims to minimize negative surprises to customers caused by sensitive content and build customer trust through appropriate interactions on sensitive topics.

About the Role

This is an exciting opportunity for a tech lead to join our team and develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work closely with talented engineers and ML scientists to put your algorithms and models into practice and contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably.

About the Team

The mission of the Alexa Sensitive Content Intelligence (ASCI) team is multifaceted:

  • Minimize negative surprises to customers caused by sensitive content.
  • Detect and prevent potential brand-damaging interactions.
  • Build customer trust through appropriate interactions on sensitive topics.

We are open to hiring candidates from various locations, but primary preference will be given to those willing to work out of Bangalore.

About You

To succeed in this role, you should have strong technical experience in building machine learning models for business applications. A PhD or Master's degree with 5+ years of applied research experience is required. Knowledge of programming languages such as C/C++, Python, Java, or Perl, as well as experience with neural deep learning methods and machine learning, are essential. Experience with modeling tools like R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, and scipy would be highly valued.

A competitive estimated salary of ₹43.71 lakhs per annum, considering the location and job requirements, reflects the company's commitment to compensating its employees fairly.



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