Data Infrastructure

Genesis AI; Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

Genesis AI is seeking an experienced Data Engineer to design, build, and maintain large-scale data pipelines for robotics foundation model training at petabyte scale. You will own core data infrastructure and collaborate with a driven team committed to advancing physical AI.

The role demands deep expertise in Python or Go, distributed systems, and production-grade infrastructure. You will standardize data models, manage storage and pipelines, and work across real-world teleoperation and

Qualifications

• 8+ years of experience designing, building, and maintaining large-scale data pipelines.
• Proficient in Python or Go and modern data tooling.
• Strong understanding of distributed systems (Spark, Kafka).
• Experience with data lakes/warehouses and object stores (S3).
• Production infrastructure experience (Kubernetes, Terraform).

Responsibilities

• Design, build, and maintain data pipelines for robotics foundation model training at petabyte scale.
• Own core data infrastructure including data models, storage, ingestion, and orchestration.
• Standardize data models and unify pipelines across real-world and synthetic datasets.
• Collaborate with a team to advance physical AI initiatives.

Skills

Python

Go

Software engineering

Data pipelines

Distributed systems

Tools

Spark

Kafka

Kubernetes

Terraform

S3

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