Machine Learning Engineer

Paris, hybrid

About Us

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 Engineer to build the models and ML systems behind our neural interface platform. You will play a pivotal role in designing, training, evaluating, and deploying models that learn from neural time-series data.

The role spans model development, data pipelines, evaluation tooling, optimization, and deployment. You will work closely with BCI researchers and product engineers to build systems that are accurate, scalable, and usable in real time. This is a hands-on role for someone who can build reliable ML systems in a fast-moving research and product environment.

In this role, you will :

• Build and maintain scalable ML pipelines for training, inference, and evaluation

• Train, evaluate, and deploy models for large-scale neural time-series data.

• Develop tools to evaluate model quality across users, sessions, and tasks

• Optimize models for robustness, latency, and real-time use

• Contribute to data processing, benchmarking, and automated evaluation workflows

• Collaborate with researchers and engineers to implement new modeling approaches

• Maintain clean, reliable, and well-documented code
  

For this role, you must have :

• Strong experience with modern ML frameworks (PyTorch, JAX or TensorFlow)

• Experience building reproducible training and evaluation pipelines.

• Strong experience with SSL, transformers, or foundation-model training

• Experience building and deploying production-grade ML systems

• Strong experience with time-series (sensor data, audio, finance, robotics, etc)

• Familiarity with cloud infrastructure, GPUs, and scalable compute environments

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