Descripción de la oferta
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.Antes de solicitar este puesto, por favor, lea la siguiente información sobre esta oportunidad que encontrará a continuación.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.Main ResponsibilitiesDesign 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 architecture, covering model performance, data and concept drift, input quality, operational behavior and relevant safety indicators. Establish thresholds, alerting, investigation workflows and criteria for scheduled or event‑triggered retraining and controlled model updates, in collaboration with Clinical, Regulatory and Software teams.Establish and maintain traceability between model development activities, software and model artifacts, verification and validation results, risk management records, approvals and releases. Ensure that MLOps processes generate the documentation and evidence required to support design reviews, change control, verification, validation and audits.Define the model packaging and deployment standards for cloud, smartphone and embedded runtimes, including model format compatibility, resource constraints, quantization or optimization requirements, runtime dependencies, version compatibility, deployment verification and rollback mechanisms. Establish clear integration contracts with Software Engineering for model handover and execution.Mandatory Qualifications and Soft skillsBachelor's or Master's degree in Computer Science, Mathematics, Physics, Electrical Engineering or a related field.At least +7 years of hands‑on MLOps engineering experience (preferably in medtech industry), designing and implementing production MLOps architectures, including model registries and versioning, experiment tracking, reproducible training pipelines, automated model validation, CI/CD for ML, containerized deployment and workflow orchestration (e.G. MLflow, Argo, Kubeflow or equivalent).Strong experience with infrastructure‑as‑code, automation and production Python workflows. Demonstrated ability to work directly with Data Scientists and ML Engineers to transform experimental research code into reproducible, tested, maintainable and production‑ready ML pipelines.Direct experience with continuous/adaptive learning systems under a change‑control or PCCP‑like framework, delivering personalized evolving models in contrast to single model validation for universal use releases.Proven experience shipping ML into a regulated or safety‑critical environment (medtech, automotive, aerospace), has designed systems to automatize design‑control processes (e.G. IEC 62304, ISO 13485, DO‑178C, ISO 26262) and has used them from the inside.Nice to haveExperience with FDA's Predetermined Change Control Plan (PCCP) guidance or equivalent adaptive‑SaMD regulatory frameworks (e.G. through an imaging/diagnostic AI company that has filed one).Background in adaptive neurostimulation, closed‑loop deep brain stimulation or closed‑loop diabetes management with insulin pumps.Experience optimizing and deploying ML models across heterogeneous environments, including cloud infrastructure, mobile devices and embedded or resource‑constrained hardware, using techniques such as quantization, pruning and model optimization.Familiarity with clinical data standards (FHIR, SNOMED CT, NWB/BIDS) and ISO 14155/MDR/ICH E9 evidence frameworks.Soft SkillsComfortable operating at the intersection of engineering and regulatory/clinical teams, translating design‑control requirements into concrete technical implementation.Pragmatic about scope and sequencing — able to prioritize the governance/compliance work that's hard to retrofit later over premature infrastructure that may need to be rebuilt.Strong written documentation skills, given the role's heavy emphasis on producing auditable change‑control and validation records.At INBRAIN, we believe that diverse perspectives drive innovation and lead to better outcomes for our team, our technology, and the patients we serve. We are committed to fostering an inclusive workplace where everyone is valued, respected, and empowered to succeed.We welcome applications from all qualified candidates regardless of race, ethnicity, nationality, gender identity or expression, sexual orientation, age, disability, religion, or any other protected characteristic. We encourage individuals from underrepresented backgrounds to apply, even if they do not meet every qualification listed, as we recognize that talent and potential come in many forms.To ensure a positive candidate experience, we aim to provide a transparent, efficient, and respectful hiring process, with timely communication and feedback throughout.This approach allows us to get to know you holistically while giving you the opportunity to learn more about our team, culture, and mission.We are looking for someone who is ready to proactively bring new ideas to the team, push boundaries, and constantly look for innovation. At INBRAIN we believe in shared success and diverse ways of thinking, here you'll learn, grow, and advance in an innovative culture.What can we offer to youA collaborative environment where innovative ideas flourish and teamwork drives us forward. At INBRAIN, we believe the power of collective intelligence is unique. You will be part of a team that thrives on open communication, knowledge sharing and mutual respect.Meaningful Work Impact: Our projects are not only exciting and challenging but also have a positive impact on the industry and society as a whole. You'll be part of a team that strives to create meaningful change.Cutting‑Edge Technology Exposure: Joining us means immersing yourself in the latest technologies and innovative solutions. You'll have access to state‑of‑the‑art tools and resources, fostering continuous learning and keeping your skills relevant in a rapidly evolving industry.Competitive salary (according to your experience/skills).Internal flexible compensation scheme.Private Health Insurance.Training bonus for professional development and access to Udemy platform.23 vacation days per year.Christmas week off.Hybrid working modality.#diverseandinclusiveWe believe that a diverse and well‑balanced workforce drives innovation. xqbhyrx At INBRAIN, we foster the inclusion of all people regardless of culture, age, gender, sexual orientation, identity and diverse abilities or any other status.
