Senior DevOps Engineer - Machine Learning Infrastructure
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:
- Fantastic opportunity to join the core group working on Speech Recognition at SoundHound
- Work on designing and implementing the distributed systems responsible for SoundHound’s Machine Learning infrastructure
- Experience in and passion for automating processes and engineering reliable and scalable distributed systems
- Expertise in any of the following tools: KuberNetes, Docker, Ceph, Jenkins, Chef, Git, MongoDB, and Nginx
- Proficient in the C++ programming language
- Strong operational experience in Linux/Unix environment and scripting languages: Bash, Perl or Python
- BS in Computer Science, Electrical Engineering, or equivalent and 5+ years of experience or MS and 3+ years of experience
- Experience with Deep Learning / Neural Network frameworks such as Caffe, Tensorflow, Torch, PyTorch, MxNet, etc.
- Experience with large-vocabulary speech recognition engines or toolkits such as HTK, Kaldi, Sphinx, Julius, FSM/OpenFST
- Experience with AWS in a production environment: EC2, S3, VPC, IAM, ELB, CloudWatch
- Familiarity with real time audio and signal processing
- Proficient in Java
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