HIR ING
Job Skills
TensorFlow, TensorFlow, BI/BA
Description
Machine Learning Engineer for Audio Processing
We seek a Lead Machine Learning Engineer responsible for developing and optimizing Machine Learning Models along with technical work including audio data processing, pre-processing, and feature extraction. The candidate will experiment with advanced Deep Learning techniques for Speech Recognition and Audio processing in collaboration with Data Scientists and Data Engineers.
Key Skills
• Strong background in various ML algorithms and proficiency in Machine Learning and Deep Learning models, particularly those applicable to audio analysis like CNNs, RNNs, and LSTMs.
• Strong foundation in Signal Processing including the understanding of audio signal spectrograms and its characteristics like sampling, frequency, and amplitude.
• Ability to pre-process audio data to remove noise, normalize, perform cleaning steps, and extract and select relevant features such as MFCCs, Zero Crossing Rate, and Spectral Centroid, among others.
• Familiarity with audio formats and codecs.
• Excellent Python skills, given its prevalence in Data Science and ML (best practices in OOP, libraries, data analysis, file handling, debugging, etc.).
• Responsive to feedback and adjustments.
• Slack/Jira/Google Docs/Git.
• Excellent problem-solving and communication skills.
• Leadership skills to guide the team through the project’s lifecycle to ensure the delivery.
Experience
• 8+ years of total IT experience.
• 3+ years of experience developing ML/DL models
• Proven experience with Deep Learning frameworks such as TensorFlow or PyTorch
• Experience using Pandas, Numpy, Matplotlib, Scikit-learn
• Expertise using Audio Processing tools and libraries including LibROSA, PyAudio, Wav2Vec, AudioKit, etc. for audio analysis, manipulation, and feature extraction.
• Experience working in Agile environments.
Background
• BSCS, BSEE, or equivalent
• AI/ML Specialization
Nice to have
• Experience with MLOps tools and practices.
• NLP techniques related to Speech-to-Text, processing, and understanding speech.
• Understanding of Pretrained Models on Large Audio Datasets.
• Familiarity with the HuggingFace Hubs
• Design of experiments or track record of research activities.
• Experience in Educational Technology
