Research Engineering Internship : Efficient AI for Brain Signals

Sigma Nova; Paris; On-site; EUR 11,000 - 17,000; Full time

Benefits offered by this job

Mentorship

GPU access

Publication opportunities

Job summary

Sigma Nova in Paris is offering an internship (March–April 2027) to work with researchers on training and deploying models for neural time series, focusing on something measurable—a benchmark, a reusable tool, or a working prototype.

You'll explore efficient models, scalable brain-signal processing, and reliable experimentation, with close mentorship, GPU access, and opportunities to contribute to a publication depending on project results.

Qualifications

• Strong Python skills with PyTorch experience.
• Solid deep learning fundamentals and interest in efficient ML systems.
• Enjoy writing clean code, debugging, and measuring what actually improves.

Responsibilities

• Work with researchers on training and deploying models for neural time series.
• Contribute to a benchmark, reusable tool, or working prototype.
• Develop and evaluate efficient models and scalable data processing pipelines.

Skills

Python

PyTorch

Deep learning

Code quality

Job description

About the internship

Sigma nova is looking for interns (March-April 2027)

You will work alongside researchers on the practical challenges of training and deploying models for complex neural time series. The project will be shaped around your interests and experience, with a focus on building something measurable and usable: a benchmark, a reusable tool, or a working prototype.

Possible project directions

1) Efficient models and deployment

Explore how to make deep learning models smaller and faster while preserving their capabilities. Possible directions include model compression, distillation, quantization, and deployment on resource-constrained devices, with opportunities to build a real-time demonstration.

2) Scalable processing of brain signals

Develop and evaluate ways to represent and process large, diverse brain-signal datasets more efficiently. You could work on reducing computational costs, improving data pipelines, or making models easier to use across different recording configurations.

3) Reliable experimentation and benchmarking

Build tools that help researchers compare models and understand practical trade-offs between performance, speed, and resource usage. This may involve reproducible evaluation pipelines, profiling, or integrating research prototypes into a robust codebase.

These are alternative directions, not a checklist: together, we will define a focused project for the internship.

Requirements and responsibilities

• Strong Python skills and experience with PyTorch


• Solid deep learning fundamentals and an interest in efficient ML systems


• Enjoyment of writing clean code, debugging, and measuring what actually improves


• Experience with model deployment, signal processing, or performance optimization is a plus


• Prior neuroscience or EEG experience is welcome, but not required

What we offer

• A research-driven startup at the interface of AI and neuroscience


• Close mentorship and collaboration with researchers and engineers


• Access to large brain-signal datasets and high-end GPU resources


• Room to shape the project and contribute to tools used by the team


• Opportunities to contribute to a publication, depending on the project and results