Level AI is a Series B funded Mountain View, CA-based startup innovating in the Voice AI space. We are backed by top VCs, technologists from Silicon Valley, and industry experts. We are on a mission to revolutionize the customer sales experience for businesses. We are innovating in speech AI, NLP/NLU, and information retrieval systems to bring customers and businesses closer to one another.

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 who've 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.