At SoundHound Inc., we believe every brand should have a voice. As the leading innovator of conversational technologies, we’re trusted by top brands around the globe. Houndify, our independent Voice AI platform, with 70,000+ users, allows brands to create custom voice assistants that deliver results with unprecedented speed and accuracy.
Our mission is to enable humans to interact with the things around them in the same way we interact with each other: by speaking naturally. We’re making that a reality through our SoundHound music discovery app and Hound voice assistant and through our strategic partnerships with brands like Mercedes-Benz, Hyundai, Deutsche Telekom, and Pandora. Today, our customized voice AI solutions allow people to talk to phones, cars, smart speakers, mobile apps, coffee machines, and every other part of the emerging ‘voice-first’ world.
Our diverse team of engineers, UX/UI designers, writers, data scientists and linguists are all passionate about creating a world with more conversations. With more than 14 years of expertise in voice technology, we have hundreds of millions of end users, and a worldwide team in six countries building solutions for a voice-first world.
About the Role:
- This is a fantastic opportunity to join the core group working on Speech Recognition at SoundHound
- Work on building large scale Statistical Language Models, a critical system in Speech Recognition
- Run experiments and tune parameters to improve Statistical Language Models
- Build prototypes to explore novel methods/algorithms to improve the Statistical Language Models
- Identify new techniques to explore, prototype them, and then implement winning ideas in production
- Proficient in one of Java or C++ or Python
- Excellent algorithms skills and ability to write efficient code
- Good understanding of Machine Learning algorithms
- Strong problem solving and communication skills
- BS/MS in Computer Science or equivalent
- 3+ years of relevant experience
- Experience building production systems based on Machine Learning lifecycle
- Experience with application of Deep Neural Network methods to Natural Language Processing problems
- Familiarity with Statistical Language Modeling
- Familiarity with MapReduce/Spark and other relevant infrastructure
- Experience working with Speech Recognition technology