Founding MLOps & Data Platform Engineer - Equity & Impact

Noïa Labs; Paris; Sur place; EUR 85 000 - 120 000; Plein temps

Avantages offerts par ce poste

Equity package

Full health coverage, employer-paid

100% reimbursement of public transport

Meal vouchers €11/day

New MacBook Pro

RTT (extra days off)

Résumé du poste

Noïa Labs, a French neurotechnology startup, is seeking a talented MLOps / Data Platform Engineer to build the data and ML infrastructure for non-invasive neural interfaces. You will own systems that ingest, version, and serve large-scale neural data, enabling fast, reproducible, and scalable model training from dataset construction through deployment.

This hands-on role spans data pipelines, ML workflows, compute infrastructure, experiment tracking, model deployment, and internal tooling,

Qualifications

• 5+ years of production-grade MLOps and data infra experience
• Strong Python software engineering
• Experience with cloud infrastructure, containers, CI/CD
• Experience with GPU workloads or distributed training

Responsabilités

• Build and maintain the platform for neural data ingestion, processing, storage, and retrieval
• Develop pipelines for dataset generation, training, evaluation, and deployment
• Create tools to 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

Description du poste

Noïa Labs, a French neurotechnology startup, is seeking a talented MLOps / Data Platform Engineer to build the data and ML infrastructure for non-invasive neural interfaces. You will own systems that ingest, version, and serve large-scale neural data, enabling fast, reproducible, and scalable model training from dataset construction through deployment.

This hands-on role spans data pipelines, ML workflows, compute infrastructure, experiment tracking, model deployment, and internal tooling,