- Published: March 2, 2021
AmplioSpeech Inc. is an innovator in digital speech-language therapy, devoted to maximizing student outcomes and empowering SLPs, by delivering Digital Speech-Language Therapy to school districts and their SLPs, students and their families, special education departments and administrators. Powered by an award-winning artificial intelligence technology, AmplioSpeech equips school districts with a speech-language therapy operating system that powers speech-language therapy in the 21st century.
As the Head of AI and NLP at AmplioSpeech, you will be responsible for designing, leading and helping to implement our cutting-edge educational technology. You will have the opportunity to impact the lives of tens of thousands of children with learning difficulties, developing solutions to engage them with differentiated, individualized therapies. Our solutions provide high-intensity interventions that analyze performance and reinforce the instruction students receive from their therapists and teachers.
Role and Responsibilities
- Plan and lead research, innovation, exploration, design and implementation of our ML/AI roadmap.
- Define and own the implementation of NLP-based ML/AI solutions, from proof-of-concept to deployment.
- Identify opportunities to leverage the company’s data towards building our roadmap.
- Improve and strengthen the underlying ML/AI technologies in our products
- Develop custom data models and algorithms using NLP, AI and signal processing.
- Develop processes and tools to monitor, analyze and improve model performance and accuracy.
- Mentor and lead the AI & algorithms team
- At least 10 years of experience working in data science
- At least 3 years of experience in managing data science team.
- 3+ years of experience in ML/AI in the field of NLP
- Advanced academic degree (Ph.D./M.Sc.) in Computer Science / Math / Physics / Electrical Engineering or related area.
- Hands-on and theoretical background in machine learning practices and techniques (clustering, decision tree learning, NN, etc), and real-world - advantages/drawbacks
- Expert implementation skills in Python, and popular machine learning frameworks (scikit-learn, TensorFlow, Keras, etc)
- Experience in deploying machine learning products in live production environments.