Senior Automatic Speech Recognition (ASR) Engineer

Summary

We are looking for a Senior Automatic Speech Recognition (ASR) Engineer to join our ASR Research & Development team. In this role, you will work on challenging research problems and develop state-of-the-art speech recognition technologies that improve real-world user experiences across multiple languages. You will combine research, experimentation, and hands-on model development to improve ASR accuracy, optimise training pipelines, and enhance language modelling capabilities. You will evaluate emerging algorithms, analyse large-scale speech and product usage data, and contribute to the continuous improvement of model architectures and training recipes. Experience training and optimising Conformer-based ASR models will be highly desirable.

Key Responsibilities

• Research, develop, train, and optimise end-to-end ASR models to improve speech recognition accuracy and robustness.
• Conduct experiments to evaluate ASR model quality and analyse the impact of hyperparameters, training strategies, and model configurations.
• Develop and improve model training recipes, fine-tuning strategies, and language modelling techniques.
• Work with Conformer-based architectures and other state-of-the-art speech recognition models, including model training and optimisation where applicable.
• Identify, curate, clean, optimise, and cluster speech and text training datasets to improve model performance.
• Analyse large-scale product usage data and speech recognition errors to identify performance gaps and opportunities for improvement.
• Apply model optimisation techniques, including pruning, efficient representations, model compression, and language model interpolation.
• Improve ASR performance across multiple languages, accents, speaking styles, and real-world acoustic conditions.
• Stay up to date with advancements in speech technology, deep learning, computational linguistics, and natural language processing.
• Design and execute research experiments, interpret results, and present findings and recommendations to the research team.
• Collaborate with cross-functional engineering, research, and product teams to translate business requirements into effective technical solutions.
• Maintain high standards of code quality, reproducibility, experimentation, and documentation.

Required Skills and Qualifications

• MSc or PhD in Computer Science, Computational Linguistics, Applied Mathematics, Electrical Engineering, or a related discipline.
• 5+ years of relevant experience in speech technology, ASR, machine learning, or a closely related research and development field.
• Strong background in speech recognition, computational linguistics, machine translation, and/or natural language processing.
• Extensive hands-on experience with end-to-end ASR systems and language modelling.
• Strong understanding of deep learning architectures, machine learning principles, statistical methods, and model evaluation techniques.
• Practical experience with model training, fine-tuning, hyperparameter optimisation, and performance evaluation.
• Proficiency in Python and shell scripting, with strong software engineering fundamentals.
• Experience working in Linux environments; familiarity with Windows-based development environments.
• Ability to analyse experimental results, investigate model errors, and identify effective solutions to complex technical problems.
• Strong analytical, problem-solving, and critical-thinking skills.
• Excellent written and verbal communication skills in English.

Preferred Skills

• Experience training, fine-tuning, and optimising Conformer-based ASR models.
• Experience with Transformer-based architectures, attention mechanisms, sequence modelling, and modern end-to-end speech recognition approaches.
• Knowledge of acoustic modelling, pronunciation modelling, decoding strategies, and speech recognition evaluation metrics such as Word Error Rate (WER) and Character Error Rate (CER).
• Experience with large-scale speech data processing, dataset selection, data augmentation, and multilingual speech modelling.
• Experience with generative AI, Large Language Models (LLMs), prompt engineering, and fine-tuning techniques.
• Expertise in one or more areas such as machine learning, statistical modelling, information retrieval, deep learning, or data mining.
• Experience with model compression, pruning, quantisation, efficient inference, or deployment optimisation.
• Familiarity with ASR frameworks and tools such as ESPnet, NVIDIA NeMo, SpeechBrain, Kaldi, or equivalent technologies.
• Fluency in a second language other than English.