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About The Opportunity : Join a high-velocity engineering team building robust, low-latency speech and voice solutions for large-scale deployments.You will design and ship state-of-the-art ASR models and production pipelinesbridging classical signal-processing foundations with modern transformer-based speech models to drive measurable product impact.Role & Responsibilities : - Lead design, training and optimisation of ASR systemsend-to-end and hybridusing transformer and sequence modeling (Wav2Vec 2.0, Whisper, CTC, attention-based encoders/decoders).- Develop and evaluate speech pre-processing and DSP pipelines (feature extraction, augmentation, denoising, VAD) to improve robustness across noisy, multilingual inputs.- Prototype and productionise model-serving solutions : containerised inference, latency optimisation, batching, and autoscaling for cloud and edge deployments.- Collaborate with data engineers and linguists to curate datasets, define annotation guidelines, and run rigorous evaluation (WER, CER, streaming metrics) and error-analysis cycles.- Implement reproducible training workflows, CI/CD for models, monitoring for drift and performance, and automation for retraining and A/B evaluation.- Mentor peers, author engineering-excellence patterns (testing, observability), and present technical results to product and stakeholder teams.Skills & Qualifications : Must-Have : - 5+ years in speech recognition or related audio ML roles with proven production impact.- Strong DSP and audio analysis fundamentals (feature engineering, spectrograms, filtering, VAD).- Hands-on experience with PyTorch and/or TensorFlow for building and training ASR models.- Practical knowledge of transformer-based speech models (Wav2Vec 2.0, Whisper) and sequence losses (CTC), plus RNN/CNN architectures.- Proficient in Python; experience with C++/Java for production deployments is highly desirable.- Experience deploying models in cloud environments (AWS/GCP) and container orchestration (Docker/Kubernetes); familiar with MLOps tooling and CI/CD.Preferred : - Background in multilingual ASR, low-resource languages, or on-device/edge inference optimisation.- Experience with large-scale data pipelines, annotation platforms, and semi-supervised / self-supervised learning workflows.- Familiarity with production monitoring (prometheus/grafana), model explainability, and privacy-preserving ML techniques.Benefits & Culture Highlights : - High-autonomy engineering culture with strong emphasis on ownership, mentorship, and career growth.- Opportunity to influence product direction and work on state-of-the-art speech models at scale.- Competitive compensation, flexible hybrid work, and learning budget for conferences and training.We are seeking a results-oriented Speech Scientist who thrives on technical ownership and delivering dependable voice AI in real-world settings.Apply if you want to push ASR boundaries and build production-grade speech systems that scale. (ref:hirist.tech)
