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# AI/ML Speech Algorithm Engineer (F/M)

- **Company:** EssilorLuxottica
- **Location:** Sophia Antipolis, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Sound Recording and Reproduction, Cloud Computing, Python (Programming Language), Machine Learning, Regression Testing, Tensorflow, Signal Processing, Pytorch, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** September 23, 2026
- **Apply:** https://careers.essilorluxottica.com/talentcommunity/apply/1440457233/?locale=fr_FR

## About the Role

* MSc or PhD in Electrical Engineering, Computer Science or a related STEM field, with real depth in deep learning and signal processing (a BSc with equivalent demonstrated depth also works).
 * 5+ years building audio or speech ML algorithms, including at least one algorithm personally carried from research into a shipped product.
 * Hands-on model development and production experience in Python, with PyTorch or TensorFlow.
 * Track record of deploying models to real-time or embedded targets under latency, memory and compute constraints, iterating on the basis of measurements rather than intuition.
 * (Preferred) Hearing-aid-class signal processing, or experience with hearables and wearable audio products.
 * (Preferred) End-to-end audio system understanding: transducers, microphones, audio ICs, acoustics, embedded DSPs and NPUs.
 * (Preferred) Familiarity with model optimization techniques - quantization-aware training, pruning, knowledge distillation, and other methods for compressing models without sacrificing performance.
 * (Preferred) Cloud-based training environments (GCP or equivalent) and experience with GPU cluster workflows.
 * (Preferred) Exposure to regulated or medical-device product development.
 * (Preferred) Publications or patents in speech, audio or applied deep learning.

## Description

* Contribute to speech AI algorithms end to end - speech enhancement, acoustic feedback cancellation, beamforming, acoustic scene recognition - from architecture exploration through to a verified feature on a shipping device.
 * Design, train and evaluate deep learning architectures against hard real-time latency, MIPS and footprint budgets, incorporating quantization, pruning and other model optimization techniques for deployment.
 * Build out the evaluation stack: objective and perceptual metrics, listening tests with audiology colleagues, and regression testing to verify model improvements.
 * Generate and curate the data the models need - noise corpora, room impulse responses and acoustic simulation, multi-microphone and binaural recordings.
 * Contribute reusable training and testing infrastructure across tasks.