The Challenge: Turn Research into Industrial IP

Sigma Nova’s growth depends on our ability to translate research into Capabilities: reusable technical building blocks (pipelines, frameworks, interpretability tools) that become permanent assets for the company and our clients.

Your mission: Transform research experiments into installed, documented, and monetizable capabilities that are ready for deployment, scaling, and client integration.

What You’ll Do

• Building the Capability Library:

  • Support the development of modular, reusable components emerging from research (e.g., EEG preprocessing pipelines, fMRI interpretability tools).
  • Act as the guarantor of versionability, documentation, and reproducibility, ensuring that research outputs can be reliably reused and extended.

• Finetuning & Client Adaptation:

  • Execute and optimise finetuning pipelines (PyTorch, Hugging Face) for diverse domains.
  • Adapt foundation models to specific client needs while maintaining performance and scalability.

• Backend & Inference:

  • Design high-performance inference servers (FastAPI, gRPC) and SDKs to expose capabilities seamlessly.
  • Optimise for low latency, scalability, and ease of integration.

• Deployment & Ops:

  • Deploy models via Docker/Kubernetes on Scaleway to ensure a frictionless “Lab to Production” transition.
  • Implement monitoring, logging, and maintenance for long-term reliability.### Key Skills

• Experience: 4+ years in ML engineering, applied research, or deployment.

• Technologies:

  • Python: Production-grade, typed, and documented code.
  • PyTorch/Hugging Face: Finetuning, optimisation, and deployment.
  • Backend/APIs: FastAPI, Flask, or gRPC for model exposure.
  • DevOps: Docker, CI/CD, cloud deployment (Scaleway/AWS).
  • Fluent English (the team speaks English in the day-to-day)

Bonus Skills

• MLOps (model versioning, monitoring, lifecycle management).
• Low-level optimisation (CUDA, memory, hardware acceleration).
• Interpretability tools (SHAP, LIME, SAEs) or client-facing experience.
• Experience working with one or more of these fields: neurology, clinical data, generative methods, and multi-modality.

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.