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Salary
≈ $57k – $127k per year (Estimated)
Location
Hybrid (Paris, France)
Seniority
Middle · 3+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Sep 15, 2026.

Overview
Company
Impact
Profile match
Sigma Nova is an artificial intelligence company that builds specialized foundation models for scientific signals, starting with brain data. Its vacancies include research engineers, research scientists in reinforcement learning and computational physics, and research interns. The company is headquartered in Paris, France.

Join our team as a Research Engineer, where you'll be at the forefront of building foundational models for scientific signals, starting with the brain. You'll be responsible for scaling pre-training, fine-tuning pretrained models, engineering multimodal data pipelines, and upholding scientific rigor and software standards. The ideal candidate will have 3-7 years of experience in applied research or R&D, mastery of PyTorch, and a strong background in data engineering.

Missions

  • Implement and supervise large-scale pre-training runs on cutting-edge architectures, ensuring convergence and performance across distributed resources.
  • Build robust pipelines to aggregate, align, and clean heterogeneous datasets, tackling the complexity of signal normalization and data versioning.
  • Establish internal leaderboards and data tests to validate progress against the state of the art, and write production-ready research code.

Profil recherché

### Requirements

* 3-7 years of experience in applied research or R&D.

* Distributed Training: Proven track record with large-scale model training.

* PyTorch Mastery: Deep knowledge of internals (memory, kernels, attention mechanisms).

* Data Engineering: Robust pipelines, versioning, and data quality.

* Scientific Rigor: Experience in research environments with high software standards.

* Fluent English (the team speaks English in the day-to-day)

### Bonus Skills

* Multimodal Data: EEG, fMRI, time-series, or sensor signals.

* Low-Level Optimisation: FlashAttention, CUDA kernels, or custom hardware.

* Cloud Infrastructure: Scaleway or similar GPU cloud environments.

* Research Publications: NeurIPS, ICML, ICLR, or equivalent.

* Data-Centric AI: Curation, alignment, and cleaning of heterogeneous datasets.

### Beyond Technical Skills:

While technical excellence is critical, we place equal importance on how we work together. We believe the best teams are built on:

* Integrity & Respect

* We are striving for honesty, kindness, and fairness. We value people who treat others with dignity and foster an environment where everyone feels heard.

* Open Communication & Humility

* Great ideas come from collaboration. We look for teammates who listen actively, communicate clearly, and approach challenges with self-awareness and humility.

* Psychological Safety & Camaraderie

* We strive to create a space where people feel safe to take risks, ask questions, and grow.

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