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описание
We are seeking a highly experienced Senior Data Scientist to lead the research, development, and scaling of our next-generation conversational AI and multimodal systems. In this role, you will focus on Speech Processing. You will own the end-to-end lifecycle of our core models — from designing massive data pipelines and human-in-the-loop labelling systems, to training, fine-tuning, and deploying state-of-the-art Automatic Speech Recognition (ASR)
models.
задачи
Speech technology (ASR): Build, train, and optimise custom ASR models for high-accuracy transcription in noisy environments.
Data processing and labelling pipelines: Architect scalable data pipelines for ingesting, cleaning, and processing large volumes of unstructured audio and text data. Design strict quality assurance rubrics and manage human-in-the-loop labelling workflows to ensure high-fidelity training datasets.
End-to-end model lifecycle: Lead the full iterative lifecycle, including data curation, baseline modelling, hyperparameter optimisation, evaluation, and production inference optimisation using techniques such as quantisation, distillation, and TensorRT.
Architectural leadership: Evaluate emerging architectures, research papers, and open-source models. Make strategic decisions on when to build custom models from scratch versus fine-tuning existing foundational models.
Cross-functional collaboration: Partner with ML Engineers, Backend Developers, and Product Managers to integrate models into user-facing applications with stringent latency and throughput requirements.
Mentorship: Guide and mentor junior data scientists and engineers, fostering a culture of rigorous scientific experimentation and robust engineering practices.
требования
Education: Master’s or Ph.D. in Speech Science, Artificial Intelligence, Computational Linguistics, Mathematics, or a highly related quantitative field.
Experience: 5+ years of industry experience in Machine Learning, Deep Learning, or Data Science, with at least 2 years specifically focused on Generative AI, NLP, or Speech domains.
Speech processing: Deep theoretical and practical understanding of acoustic modelling, ASR architectures such as Whisper, Conformer, wav2vec, Nemotron
Data engineering for AI: Extensive experience designing data curation strategies, managing data annotation projects, and using tools for audio processing and text normalisation.
Programming and frameworks: Expert-level proficiency in Python and PyTorch.
Experience deploying models on edge devices or optimising ML models for low-compute environments.
Published research in tier-1 conferences such as ACL, Interspeech, ICASSP, or NeurIPS.
Experience with multimodal model architectures, including audio-text native models.
Familiarity with cloud ML ecosystems such as AWS SageMaker, GCP Vertex AI, or Azure ML.
