Research Engineering Internship
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About the internship
Sigma nova is looking for interns (starting in Q1 / early Q2 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.
Preferred experience
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
Recruitment process
Prescreen recruiter
Technical screen with a Research Engineer
Onsite interviews (coding / research discussion)
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