Role and Responsibilities
As a Speech/Audio AI - Intern, you will:
Innovate & develop state-of-the-art solutions to industry relevant problems in the field of on-device speech and audio AI
Actively propose and prototype novel research ideas/solutions considering real-world constraints
Work on complex systems and develop research ideas into production ready software
Incorporate software engineering practices at both research and development stages
Communicate and disseminate research results via papers and/or reports
Required:
Perusing PhD degree in ML/AI, Computer Science/Engineering, Mathematics, Statistics, or related disciplines
Strong fundamentals in machine learning and artificial intelligence
First author publications in top ML/AI conferences/journals (e.g., ICML, NeurIPS, ICLR, CVPR, SysML, ICASSP, INTERSPEECH, IEEE TPAMI, IEEE IoT, IEEE TNNLS, JMLR or similar)
Machine Learning experience in least one of the following domains:
Automatic Speech Recognition (ASR)
Speech Enhancement
Audio Processing and Generation
Generative AI – especially Audio domain
Demonstrated success:
Strong development skills with Python and/or C/C++ is required.
Experience with programming using popular machine learning frameworks such as PyTorch and/or Tensorflow
Creating comprehensive and well-written documentation.
Familiarity with software engineering practices and tools such as Git
Excellent communication, teamwork and a results-oriented attitude
Proficiency in problem solving and debugging
Desirable:
Experience using open source speech processing toolkits such as HuggingFace, SpeechBrain, ESPnet, Kaldi, NeMo, etc.
A proven track record of developing complex training and inference pipelines
Knowledge of embedded and/or distributed machine learning methods and tools
Experience with model optimization, profiling and performance improvement of AI pipelines
Experience and knowledge in AI based audio generation and processing
Contribution to open source software libraries
Contract Type:6 month internship
Start date:Immediate (within 1 month of accepting the role)
Hybrid Working:3 days onsite and 2 days working from home
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