Machine Learning Engineer - Language Modeling (German)
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Role details
Tech stack
Requirements
- Proficient in Java and at least one scripting language
- Excellent algorithms skills, and ability to write efficient code
- Deep fluency with academic fields relevant to Statistical Language Modeling
- Experience building production systems based on Machine Learning lifecycle
- Full professional proficiency in German
- BS/MS in Computer Science or equivalent
Nice-to-haves:
- Familiarity with MapReduce/Spark and other relevant infrastructure
- Familiarity with DNN, class-based language modeling, and other relevant techniques
- Experience working with large text corpora and speech-related technologies
- Experience building large scale crawlers
- Experience working with Lucene, Nutch, Elasticsearch
Benefits & conditions
SoundHound is creating and productizing transformative technologies that improve life. Join us! After amassing a global user community well over 300 million strong, a large portfolio of core technologies and award winning products….we’re just getting started. At SoundHound we value creativity, innovation, hard work, open communication and fast iteration, which allow us to act on valuable feedback from employees and users alike. SoundHound’s culture is one of impromptu coffee breaks, less-impromptu fitness sessions, group lunches, and weekly happy hours. We offer a competitive salary, SoundHound stock options, unique camaraderie, catered lunches, and the opportunity to call a company home that’s simultaneously changing the way we discover music AND interact with machines. Yep, it’s that cool. 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
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