Machine Learning Engineer - Speaker Verification

This is a remote position.

• Design implement and optimize machine learning pipelines for speaker verification.
  
• Collaborate closely with crossfunctional distributed teams to integrate features into edge devices.
  
• Conduct detailed exploratory data analysis extracting actionable insights for robust use cases in target embedded hardware platforms.
  
• Work closely with software engineers to deploy and finetune machine learning models ensuring optimal performance on Edge AI chipsets.
  
• Participate in the complete development lifecycle from model design to deployment focusing on the unique requirements of our applications.
  
• Ability to develop and debug productionquality code
  
• Familiarity with continuous integration models and unit test development
  
• Scientific thinking ability to design experiments and training frameworks and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.
  

Requirements

• Advanced degree (MSc or PhD) in Computer Science Machine Learning Speech Science or a related Data Science field.
  
• Proven experience developing machine learning models for voice biometrics speech modelling Speaker
  
• Recognition Verification Authentication Diarisation or Identification. Expertise in Python Frameworks and toolkits (TensorFlow PyTorch TFlite ONNX NumPy ScikitLearn Pandas)
  
• Strong understanding of signal processing techniques acoustic modeling and resourceefficient model design for speaker verification.
  
• Familiarity with deep learning architectures (TDNN RCNN) optimized for speaker verification in Edge AI environments.
  
• Excellent problemsolving skills with the ability to adapt models to meet computational constraints specific to speaker verification applications.