Data Infrastructure

Genesis AI; Paris; On-site; EUR 90,000 - 120,000; Full time

Job summary

Genesis AI 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 layers, and standardize pipelines across teleoperation and synthetic datasets.

Ideal candidates have 8+ years of experience, strong Python/Go skills, and deep knowledge of Spark, Kafka, and data lakes.

Qualifications

• 8+ years of experience building/Pipeline infrastructure
• Experience with large-scale data pipelines and engineering
• Strong knowledge of distributed systems and data storage technologies

Responsibilities

• Design, build, and maintain large-scale data pipelines (batch and streaming) for robotics foundation model training and evaluation
• 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 to build general-purpose Physical AI

Skills

Python

Go

Data pipelines

Distributed systems

Batch processing

Streaming

Tools

Spark

Kafka

S3/Data lakes

Kubernetes

Terraform

Job description

What You’ll Do

• 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

What You’ll Bring

• Excellent software engineering skills (Python, Go, or similar)
• Extensive experience designing, building, and maintaining large-scale data pipelines (8+ years)
• Deep understanding of distributed systems (Spark, Kafka, or similar)
• Extensive experience with data storage technologies (data lakes, warehouses, object stores like S3)
• Experience running and maintaining production‑grade infrastructure (Kubernetes, Terraform)
• Bonus: Experience supporting AI systems, in particular embodied AI like self-driving