Spydra - Data Scientist - Automatic Speech Recognition
Job Description We are looking for an exceptional Data Scientist with deep expertise in speech technologies, advanced NLP, and LLM fine-tuning to join our cutting-edge AI research team. In this pivotal role, you will be responsible for building and optimizing state-of-the-art machine learning pipelines that drive intelligent audio and language-based products. Your work will directly contribute to the development of next-generation AI solutions that are privacy-focused, high-performance, and built for scale. Key Responsibilities Develop and deploy real-time ASR pipelines, leveraging models like Whisper, wav2vec2, or custom speech models. Design and implement robust intent detection and entity extraction systems, utilizing transcribed speech, keyword spotting, and semantic pattern recognition. Fine-tune LLMs and transformer architectures (BERT, RoBERTa, etc.) for tasks including intent classification, entity recognition, and contextual comprehension. Optimize end-to-end pipelines for mobile and on-device inference, employing tools like TFLite, ONNX, quantization, and pruning to achieve low-latency performance. Collaborate closely with AI product teams and MLOps engineers to ensure seamless deployment, continuous iteration, and performance monitoring. Required Technical Skills Hands-on experience with ASR models (Whisper, wav2vec2, DeepSpeech, Kaldi, Silero), with a focus on fine-tuning for Indian languages and multilingual scenarios. Strong command of NLP techniques such as keyword spotting, sequence labeling, masked token prediction, and rule-based classification. Proven track record in LLM and transformer fine-tuning for NER, intent detection, and domain-specific adaptation. Expertise in speech metadata extraction, feature engineering, and signal enrichment. Proficiency in model optimization methods like quantization-aware training (QAT), pruning, and efficient runtime deployment for edge devices. Excellent Python skills with proficiency in PyTorch or TensorFlow, along with solid experience in NumPy, pandas, and real-time data processing frameworks. Qualifications Bachelors or Masters degree in Computer Science, Electrical Engineering, Data Science, or a related technical field. Academic or industry background in speech processing, ASR, telecom analytics, or applied NLP is highly desirable. Portfolio showcasing real-world speech/NLP projects, open-source contributions, or published research will be a strong advantage. Experience 3 to 6+ years of applied experience in speech AI, NLP for intent detection, or machine learning model development. Proven success in building, deploying, and optimizing ML models for real-time, low-latency environments. Contributions to leading open-source projects like openai/whisper, mozilla/DeepSpeech, or facebook/wav2vec2 are highly valued. (ref:hirist.tech) Show more Show less
