Machine Learning Engineer (Speech) - AI

Level AI was founded in 2019 and is a Series C startup headquartered in Mountain View, California. Level AI revolutionizes customer engagement by transforming contact centers into strategic assets. Our AI-native platform leverages advanced technologies such as Large Language Models to extract deep insights from customer interactions. By providing actionable intelligence, Level AI empowers organizations to enhance customer experience and drive growth. Consistently updated with the latest AI innovations, Level AI stands as the most adaptive and forward-thinking solution in the industry.

Empowering contact center stakeholders with real-time insights, our tech facilitates data-driven decision-making for contact centers, enhancing service levels and agent performance. As a vital team member, 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. Ready to redefine possibilities? Join us!

Roles and Responsibilities :

• Work on problems arising in speech-to-text pipelines, such as transcription, diarization, voice activity detection.
• Train, deploy and maintain scalable automatic speech recognition pipelines to power Level AI’s ASR engine.
• Keep abreast of SOTA techniques in ASR and exchange knowledge with colleagues.
• Work with other team members to develop the 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 :

• 2+ years of experience and a bachelor's degree in Computer Science or a related field from a tier-1 engineering institute.
• Experience deploying Deep Learning based models for speech recognition pipelines.
• Awareness of state-of-the-art research in speech recognition and signal processing.
• Algorithm and model development experience for large-scale applications.
• Strong knowledge of Machine Learning fundamentals.
• Experience with Python and Pytorch.