At Inbrain Neuroelectronics, we built our company around a simple belief: outstanding impact needs outstanding people.

Our mission is to transform the treatment of neurological disorders through neuroelectronic therapies that are safer, smarter, and more human‑centered. We work at the intersection of neuroscience, engineering, and advanced materials, but what truly drives us is the impact our work can have on patients’ lives.

Our culture is grounded in Integrity First—we hold ourselves to the highest ethical standards because the people we serve deserve nothing less. We are Human Powered, valuing empathy, collaboration, and the diverse perspectives that make our ideas stronger.

We encourage everyone to think like Game Changers, challenging assumptions and pushing boundaries to unlock breakthroughs that once felt out of reach.
We act with Conscious Impact, aware that every decision—from research to clinical translation—shapes real futures. And we embrace a Growth Mindset ,creating an environment where learning is continuous, curiosity is celebrated, and people are supported to stretch beyond their comfort zones.

At Inbrain, you’ll join a team united by purpose and energized by possibility. If you’re looking for a place where your talent can contribute to meaningful change—and where you can grow while helping others thrive—you’ll feel at home here.

About the Role

We are scientists, doctors, techies and humanity lovers, with the mission of pioneering real time precision neurology to cure brain-related disorders. INBRAIN harnesses the extraordinary material properties of Graphene, the world’s thinnest and nobel-prize winning material, to build high resolution neural systems.



Our mission is to decode and modulate neural networks to restore people's lives.

The Principal MLOps Architect will design and own the end-to-end model lifecycle architecture and governance framework that enables INBRAIN's data scientists and ML engineers to take adaptive neurostimulation and Brain-Computer Interface models from research into regulated clinical products.

The role defines how models are developed, versioned, validated, packaged, released, monitored and updated across cloud, smartphone and embedded runtimes. It establishes the MLOps pipelines, validation gates, traceability and evidence required to ensure that every model release is reproducible, controlled and aligned with the intended clinical use. It works closely with the Software team, which owns and maintains the underlying model runtime infrastructure.

The role bridges MLOps engineering with medical-device design-control practice: it defines how models are versioned, validated, and promoted, and builds the change-control framework (aligned with a PCCP-style approach) that governs how adaptive, continuously-learning models can be safely updated post-deployment.

This is a foundational, cross-functional role within the Data Intelligence team. It works closely with Data Scientists, ML Engineers and Data Engineers to establish reproducible model development and delivery practices.





The role partners with Clinical, Regulatory and Product teams to translate clinical objectives, intended use and regulatory requirements into model lifecycle controls. It collaborates with Systems Engineering to ensure models meet system-level requirements and with Software Engineering to define deployment interfaces, runtime requirements and release processes across cloud, smartphone and embedded environments.

What You'll Do

Main

Responsibilities:

- Design and own the model lifecycle traceability framework, linking every model version to its source code, training datasets, data provenance, preprocessing and feature definitions, training configuration, evaluation results, risk assessments and applicable clinical evidence. Ensure that model artifacts and their supporting evidence are reproducible and retrievable throughout the product lifecycle.
- Lead the technical implementation of a controlled model-change framework, aligned with applicable PCCP requirements and design-control processes. Define permitted modification types, operating boundaries, acceptance criteria, risk-based validation protocols, approval requirements and release evidence for adaptive models. Partner with Clinical, Regulatory, Product and Software teams to ensure that model updates remain within approved safety and performance limits.
- Own and configure the CI/CD pipeline for training, validating, and promoting models, running on infrastructure provided by the Software team, with promotion gates across Development, Testing, Acceptance and Production (DTAP) tiers.
- Define and implement the model monitoring and post-deployment surveillance architectur

📌 Principal MLOps Architect (Barcelona)
🏢 Inbrain Neuroelectronics
📍 Barcelona