What you’ll be doing:

• Train Speech Recognition Acoustic, Language, Punctuation models.
• Measure and benchmark model performance.
• Maintain ASR model evaluation system.
• Analyze model accuracy and bias and recommend the next course of action & Improvements.
• Improve processes for speech data processing, augmentation, filtering & ASR Training sets preparation.
• Gather knowhow on speech datasets for training & evaluation.
• Characterize performance and quality metrics across platforms for various speech AI components.
• Collaborate with various teams on new product features and improvements of existing products.
• Participate in developing and reviewing code, design documents, use case reviews, and test plan reviews.
• Help innovate, identify problems, recommend solutions and perform triage in a collaborative team environment.

What we need to see:

• Bachelor's degree or Master’s degree (or equivalent experience) or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math.
• Native or near-native fluency in a non-English language - Spanish / Mandarin / German / Japanese / Russian / French / UK English / Arabic / Hindi / Korean / Italian / Portuguese
• Excellent programming skills in Python as well as strong fundamentals in Programming, optimizations and Software design.
• Strong knowledge of ML/DL techniques, algorithms and tools with exposure to CNN, RNN (LSTM), Transformers.
• Strong knowledge of RNNT and CTC decoders. 
• Know how of Deep learning applications to Speech and NLP.
• Hands-on experience on Speech Technologies like Automatic Speech Recognition, Speech Command detection, Text to Speech, Speaker Recognition and Identification, speaker diarization, Noise robustness Techniques, Voice activity detection, End of utterance detection etc.
• Experience with Training acoustic models.
• Experience with KenLM, OpenLM and other tools to create Language models.
• Experience with “PyTorch” Deep Learning Frameworks.
• Exposure to basic speech digital signal processing and feature extraction techniques like FFT, MFCC, Mel Spectrogram, etc.
• General background around version control and code review tools like Git, Gerrit, Gitlab.

Ways to stand out from the crowd:

• Strong C++ programming skills.
• Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT
• Background with Dockers and Kubernetes
• Background with deploying machine learning models on data center, cloud, and embedded systems