Paris Data Infrastructure Engineer - Petabyte-Scale Pipelines

Genesis; Paris; Sur place; EUR 85 000 - 140 000; Plein temps

Résumé du poste

Genesis in Paris is seeking a senior data engineer to design, build, and maintain large-scale data pipelines for robotics foundation model training and evaluation. You will own core data infrastructure including data models, storage, ingestion, and orchestration, collaborating with a team to standardize processing across real-world teleoperation and synthetic simulation datasets.

The ideal candidate has 8+ years in data engineering, deep distributed systems knowledge (Spark, Kafka), and hands-on

Qualifications

• 8+ years of experience designing and maintaining large-scale data pipelines.
• Strong knowledge of distributed systems (Spark and Kafka).
• Proficient in Python or Go.
• Experience with data storage technologies (S3, data lakes, warehouses).
• Experience running production-grade infrastructure (Kubernetes, Terraform).
• Bonus: experience with AI systems, embodied AI or robotics data.

Responsabilités

• Design, build, and maintain large-scale data pipelines (batch and streaming) for robotics foundation model training and evaluation at petabyte scale.
• Own core data infrastructure: data model, storage systems, ingestion pipelines, transformation frameworks, and orchestration layers.
• Standardize data models and unify processing pipelines across real-world teleoperation and synthetic simulation datasets.
• Collaborate with a team of driven individuals committed to building general-purpose Physical AI.

Connaissances

Python

Go

Data pipelines

Distributed systems

Embodied AI

Outils

Kubernetes

Terraform

Spark

Kafka

S3

Data lakes

Warehouses

Description du poste

Genesis in Paris is seeking a senior data engineer to design, build, and maintain large-scale data pipelines for robotics foundation model training and evaluation. You will own core data infrastructure including data models, storage, ingestion, and orchestration, collaborating with a team to standardize processing across real-world teleoperation and synthetic simulation datasets.

The ideal candidate has 8+ years in data engineering, deep distributed systems knowledge (Spark, Kafka), and hands-on