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MLOps - Data Platform Engineer

Noïa Labs
Paris (75)

Postuler rapidement

Détails de l'emploi

Temps plein
Il y a 7 heures

Qualifications

CI/CD
Infrastructure cloud
Production
Python

Description complète du poste

MLOps - Data Platform Engineer

Paris, hybrid

Noïa Labs is an early-stage neurotechnology company building the next generation of human-AI interfaces.

We are working on one of the most ambitious problems in human-AI interaction: creating a more natural way for people to control, guide, and collaborate with AI systems by relying directly on brain activity. Our approach combines optimized non-invasive neural sensors with large-scale AI models trained across many users to decode human intent from brain signals, without surgery. Noïa Labs was founded by the team behind NextMind (acquired by Snap) and is backed by tier-1 investors.

We are at the beginning of the journey and are building a team of outstanding engineers and scientists where each person can have a major impact on the product and technology.

Role summary

We are seeking a talented MLOps - Data Platform Engineer to build the data and ML infrastructure behind our non-invasive neural interface. You will own the systems that ingest, version, and serve large-scale neural data, making model training fast, reproducible, and scalable from dataset construction and orchestration through distributed training and deployment.

This is a hands-on role for someone who enjoys building reliable infrastructure for fast-moving ML and research teams. The role spans data pipelines, ML workflows, compute infrastructure, experiment tracking, model deployment, and internal tooling, while working closely with ML engineers, neuroscientists, software engineers, and product teams.

In this role, you will:

Build and maintain the platform for neural data ingestion, processing, storage, and retrieval
Develop pipelines for dataset generation, training, evaluation, and deployment
Create tools that help ML engineers and scientists find data, run experiments, compare models, and reproduce results
Manage cloud and GPU compute for scalable ML workloads
Improve data quality, metadata, versioning, monitoring, and traceability
Work with software and ML teams to integrate models into the platform
Help define standards for reliability, reproducibility, privacy, and security

For this role, you must have:

5+ years of experience building production-grade MLOps and data infrastructure
Strong software engineering experience, especially in Python
Experience with cloud infrastructure, containers, CI/CD, and production systems
Experience supporting GPU workloads or distributed training
Experience with ML tooling for training, evaluation, tracking, or deployment
Strong understanding of data quality, monitoring, versioning, and reproducibility
Strong ownership, practical problem-solving skills, and attention to detail

Ideally, you have:

Experience with time-series data such as biosignals, sensor data, audio, video, or robotics
Experience with real-time streaming, edge buffering, device-to-cloud synchronization, or intermittent connectivity
Experience in health, medical device, regulated, privacy-sensitive, or research environments

In case of any doubts or questions, please contact - [email protected]

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