Machine Learning Scientist - Asr

Machine Learning Scientist – Asr

2+ years experience in building speech recognition systems
Strong research background, as demonstrated through papers and a graduate degree (MS or PhD) in Electrical Engineering, Computer Sciences, Mathematics, or equivalent experience with a specialization in speech recognition or machine learning
High-impact publications at peer-reviewed speech conferences (e.g. ICASSP, Interspeech, SLT) or NLP/ML conferences (NAACL, ACL, NeurIPS, ICML, ICLR)
Significant real-world impact, as demonstrated through open-source contributions and deployed technology
Strong programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production
Experience with deep learning libraries (e.g. PyTorch, Jax, Tensorflow) and platforms, multi-GPU training, and statistical analyses of observational and experimental data
Deep knowledge of ASR technologies such as acoustic modeling, language modeling, neural networks, HMM, WFST, and feature extraction
Hands-on experience in ASR tool kits such as Sphinx, Kaldi, HTK, or Julius

Advance the state of the art in medical ASR, in areas including accurate transcription of clinical conversations, support of a wide variety of languages and dialects, speaker diarization, domain-specific language modeling, robust handling of diverse accents and noise conditions, and development of novel evaluation and experimentation techniques
Actively contribute to the wider research community by sharing and publishing original research
Research and implement algorithms and methods for long-form and real-time speech recognition
Perform data preprocessing and define performance measures based on development and test sets
Implement and improve tools, algorithms, and prototypes
Be responsible for measuring and optimizing the quality of your algorithms
Help to define important problems, identify appropriate baselines, develop state-of-the-art methods, and ship them into production
Dial deeply into real-time feedback from clinicians to guide further refinements and innovations
Be results-oriented in the face of ambiguous problems and uncertain outcomes

Research work or publications pertaining to applying deep learning methods to speech recognition
Deep fluency in academic fields relevant to speech recognition