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Je souhaite recevoir la dernière alerte Emploi pour les postes de ce type : lab engineer (paris, a8)

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Machine Learning Scientist

Noïa Labs
Paris (75)

Postuler rapidement

Détails de l'emploi

Temps plein
Il y a 7 jours

Qualifications

TensorFlow
Informatique
Niveau Doctorat
Traitement de signal
PyTorch
Déménagement
Machine learning
Deep learning
Détecteur

Description complète du poste

Machine Learning Scientist

Paris, hybrid

Noïa Labs is an early-stage neurotechnology company building the next generation of human-AI interfaces.

We are working on one of the most ambitious problems in human-AI interaction: creating a more natural way for people to control, guide, and collaborate with AI systems by relying directly on brain activity. Our approach combines optimized non-invasive neural sensors with large-scale AI models trained across many users to decode human intent from brain signals, without surgery. Noïa Labs was founded by the team behind NextMind (acquired by Snap) and is backed by tier-1 investors.

We are at the beginning of the journey and are building a team of outstanding engineers and scientists where each person can have a major impact on the product and technology.

Role summary

We are seeking a talented Machine Learning Scientist to develop the modeling approaches behind our non-invasive neural interface. You will work on learning from neural and multimodal time-series data to help infer human intent and enable new forms of interaction with AI systems. This is a hands-on research role for someone who enjoys developing new ML methods, running rigorous experiments, and turning promising ideas into working prototypes.

The role spans representation learning, self-supervised learning, neural decoding, model evaluation, personalization, and real-time adaptation, while working closely with ML engineers, neuroscientists, software engineers, and product teams.

In this role, you will:

Develop new machine learning approaches for neural and multimodal time-series data
Design experiments, benchmarks, and ablations to evaluate model performance, robustness, and generalization
Translate research ideas into working prototypes that can be tested on real data and in user studies
Collaborate with BCI scientists and ML engineers to turn neuroscience-informed modeling ideas into reliable training and inference workflows.

For this role, you must have:

5+ years of experience in machine learning research, applied ML research, or a related field
PhD, MS, or equivalent research experience in machine learning, computer science, computational neuroscience, applied mathematics, physics, electrical engineering, or a related field
Strong experience with deep learning for time-series, sequential, multimodal, or sensor data
Strong experience with PyTorch, JAX, TensorFlow, or similar frameworks
Strong understanding of representation learning, self-supervised learning, transformers, generative models, or foundation models
Experience designing rigorous ML experiments, benchmarks, ablations, and evaluation protocols

Ideally, you have:

Experience with neural or physiological time-series such as EEG, MEG, ECoG, EMG, eye tracking, PPG, or audio
Experience with online learning, personalization, calibration, domain adaptation, or transfer learning
Experience with real-time inference, edge deployment, or low-latency ML systems
Publications or strong research output in machine learning, computational neuroscience, signal processing, or related fields
Experience working in a fast-moving startup or research-to-product environment

In case of any doubts or questions, please contact - [email protected]

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