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
Skills Required
- 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)
- Experience supporting AI systems, in particular embodied AI like self-driving
What We Do
Genesis AI is a global full-stack robotics company developing general-purpose robots with human-level intelligence and capabilities. It aims to build foundational AI models that automate repetitive tasks across applications such as lab work and housekeeping. The company uses a proprietary physics engine to generate synthetic physical-world data, helping train robotics models for diverse real-world environments, and operates across Paris and Silicon Valley.
