AI/ML Engineer - Personalised Sound Detection

About the role

Soundfr helps people who are deaf or hard of hearing stay aware of important sounds at home by recording sounds such as doorbells, appliances or a baby crying to train a personalised sound recognition model. Its connected microphone detects these sounds and the app alerts the user, and it also provides speech-to-text tools. The AI/ML Engineer works directly with the CTO to develop and improve the ML system behind the app and microphone, building on an existing product with room to influence how models are trained, evaluated and deployed.

Key responsibilities

• Develop and improve pipelines for training personalised models from user recordings.
• Improve data preparation, audio preprocessing and training datasets.
• Design evaluations that reflect real use and make improvements measurable.
• Investigate detection failures and use the findings to improve models and training.
• Optimise and deploy models to mobile and edge devices, balancing detection performance, latency and memory use.
• Follow changes through deployment and assess how they perform in practice.

Required profile

• Strong Python skills and practical experience building ML systems.
• Experience taking models through data preparation, training, evaluation and deployment.
• A systematic approach to experiments, debugging and working with imperfect data.
• Solid software engineering skills and the ability to turn experiments into maintainable production code.

Required skills

• Experience with a framework such as TensorFlow or PyTorch.
• Advantageous: audio ML, signal processing, learning from small datasets, model quantisation or on-device inference.

What we offer

• Ownership of meaningful parts of the ML system and influence over technical decisions.
• Close collaboration with the CTO and a direct link between your work and user feedback.
• Flexible hybrid work in Estonia.
• A competitive salary and employee share options.

Candidates apply through LinkedIn or by sending their CV to the company; applicants are asked to briefly describe an ML project they have built or deployed and their contribution to it.