Machine Learning Engineer (Speech) - AI

Job highlights

• Requirements : . Bachelors in Computer Science or Electrical Engineering or related fields

• Awareness of state of the art research in Speech NLP domain is a must.

• Hands on experience on building CTC Attention based encoder-decoder models for ASr . Hands-on experience with Python,Linux and a Deep Learning framework like Pytorch / Tensorflow

• As a critical member of the team, your work will be cutting-edge technologies and will play a high-impact role in shaping the future of AI-driven enterprise applications

• You will directly work with people whove worked at Amazon, Facebook, Google, and other technology companies in the world

• With Level AI, you will get to have fun, learn new things, and grow along with us

Roles and Responsibilities :

• Work on problems arising in speech-to-text pipelines, such as voice activity detection, transcription, automatic speech recognition (ASR) speaker diarization (SD),.
• Train, deploy and maintain scalable speech-to-text pipeline to power Level AI s ASR engine.
• Keep abreast with SOTA techniques in your area and exchange knowledge with colleagues.
• Work with other team members to develop architecture design of systems.
• Ability to independently conduct experiments with model architectures, training schemes, and approaches proposed in ASR literature.
• Work in an agile environment to deliver high-quality products.

Requirements :

• Bachelors in Computer Science or Electrical Engineering or related fields.
• Strong knowledge of Machine Learning fundamentals and Deep learning architectures like Transformer, Conformer etc
• Hands on experience on building CTC Attention based encoder-decoder models for ASr
• Hands-on experience with Python, Linux and a Deep Learning framework like Pytorch/Tensorflow.
• Strong software engineering abilities so as to convert research into production worthy deployments.
• Experience in building text-to-speech (TTS) punctuation restoration for ASR noisy transcripts is a plus.
• Awareness of state of the art research in Speech NLP domain is a must.