Speech AI researcher

DESCRIPTION

Role summary

We at SPIRE Lab, IISc, are seeking a Research Intern for 2 months with a possibility to convert into a Speech AI Research Associate to conduct applied and foundational research in signal processing and machine learning, with a particular focus on speech-related AI models. The role will contribute to the development, evaluation, and publication of innovative methods for speech and audio understanding, enhancement, separation, generation, coding, and real-time deployment.

The successful candidate will work collaboratively with researchers and engineers to translate research ideas into reproducible prototypes, experimental results, and technical publications.

Key responsibilities

• Research and develop machine-learning models for speech and audio applications, such as speech enhancement, source separation, noise reduction, acoustic scene analysis, speaker modeling, speech coding, or speech generation.
• Apply signal-processing principles to design, analyze, and improve speech and audio algorithms.
• Build, train, fine-tune, and evaluate deep-learning and foundation-model approaches using speech/audio datasets.
• Design rigorous experiments, establish evaluation pipelines, and communicate findings through reports, publications, presentations, and demonstrations.
• Implement prototypes and collaborate on efficient or real-time deployment where relevant.
• Contribute to research proposals, technical documentation, open-source codebases, and intellectual-property activities as appropriate.
• Collaborate effectively across multidisciplinary teams and mentor junior researchers or students where needed.

Mandatory qualifications and technical skills

• Bachelor’s degree or above in Electrical/Electronics Engineering, Computer Science, Artificial Intelligence, or a closely related discipline.
• Strong foundation in digital signal processing, including time-frequency analysis, filtering, spectral methods, feature extraction, and statistical signal processing.
• Demonstrated experience in machine learning and deep learning for speech, audio, or other sequential signal modalities.
• Proficiency in Python and practical experience with at least one modern ML framework, preferably PyTorch; TensorFlow or JAX experience is also relevant.
• Familiarity with model training, data preprocessing, experiment tracking, error analysis, and objective evaluation methods.
• Understanding of core speech/audio ML tasks such as automatic speech recognition, speech enhancement, speaker separation, speech synthesis, audio classification, or audio coding.
• Ability to read research literature critically and independently develop, validate, and document research ideas.
• Decent written and verbal communication skills in English, including the ability to explain technical work clearly, write research reports, and collaborate constructively in a team.
• Working in Linux-based environments

Nice-to-have technical skills

• Experience with transformers, self-supervised learning, large speech/audio foundation models, GANs, diffusion models, or neural codecs.
• Knowledge of speech and audio toolkits such as torchaudio, librosa, Kaldi, ESPnet, SpeechBrain, Hugging Face, or similar frameworks.
• Experience with acoustic modeling, acoustic-articulatory modeling, spatial audio, binaural processing, beamforming, microphone arrays, or hearing-device algorithms.
• Familiarity with speech datasets, data collection, annotation, augmentation, quality control, and responsible dataset use.
• Docker, Git, and reproducible research workflows.
• A record of peer-reviewed publications, conference presentations, patents, or open-source contributions in speech, audio, machine learning, or signal processing.
• Experience supervising students, collaborating with external partners, or contributing to interdisciplinary research projects.

Desired personal attributes

• Curious, rigorous, and comfortable working on open-ended research problems.
• Able to balance scientific depth with practical experimentation and implementation.
• Collaborative, organized, and receptive to feedback.
• Comfortable presenting results to both technical and non-technical stakeholders.

Selection Procedure

• 2 month paid (15000 INR per month) research Internship with potential to become Research Associate based on performance

Compensation

• Compensation shall be determined by qualification, experience and internship performance

Interested candidates are encouraged to apply here: https://forms.gle/ysFsR6xYHkaCzQin7

We encourage enthusiastic candidates with Engineering and management background to apply.

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