Research Engineer

Sigma Nova; Paris; Hybrid; EUR 70,000 - 120,000; Full time

Job summary

Sigma Nova is building foundational models for scientific signals (EEG, fMRI, ultrasound) and aims to extend to other domains. The architect of our Data & Training Factory will scale pre-training, fine-tune models, and engineer robust multimodal pipelines with a focus on data integrity and reproducibility.

The role requires 3–7 years in applied research, deep PyTorch knowledge, distributed training experience, and strong English communication for collaboration across teams.

Qualifications

• 3–7 years of experience in applied research or R&D.
• Experience with large-scale training and distributed resources.
• Deep knowledge of PyTorch internals (memory, kernels, attention).
• Strong data pipelines, data quality, and versioning practices.
• Track record in research environments with high software standards.
• Fluent English; day-to-week collaboration in English.

Responsibilities

• Scale pre-training to new frontiers on transformers and long-context models.
• Fine-tune pretrained models on open-source and proprietary data.
• Engineer robust multimodal data pipelines for heterogeneous datasets.
• Enforce scientific rigor with internal leaderboards and data tests.
• Write production-ready research code: typed, tested, documented.

Skills

R&D experience

Distributed training

PyTorch mastery

Data engineering

Scientific rigor

English fluency

Job description

We’re building foundational models for scientific signals, starting with the brain (EEG, fMRI, ultrasound), and we aim to extend these approaches to other complex domains.

We work with temporal signals, collected under real-world constraints (e.g., clinical settings with limited sample sizes): our data is noisy and heterogeneous.

What the Work Looks Like

As the architect of our “Data & Training Factory,” you’ll:

• Scale Pre-training to New Frontiers

  • Implement and supervise large-scale pre-training runs on cutting-edge architectures (Transformers, SSMs/Mamba, long-context windows).

  • Ensure convergence and performance across distributed resources (Scaleway/GENCI).

  • Practice “Deep Debugging”: Diagnose why models converge—or fail—at a mathematical and technical level.

• Fine-tune pretrained models on both open-source and proprietary data

• Engineer Multimodal Data Pipelines

  • Build robust pipelines to aggregate, align, and clean heterogeneous datasets (EEG/fMRI).

  • Tackle the complexity of signal normalisation, temporal/spatial alignment, and data versioning.

  • Champion Data-Centric AI: Ensure every byte feeding our models is pristine and traceable.

• Uphold Scientific Rigor & Software Standards

  • Establish internal leaderboards and data tests to validate progress against the SOTA.

  • Write production-ready research code: Typed, tested, and documented by default.

  • Collaborate with the team on research papers.

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-week).

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.