Data Infrastructure

Genesis AI; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Genesis AI in London seeks an experienced data infrastructure engineer to design, build and maintain large-scale data pipelines for robotics foundation model training and evaluation at petabyte scale.

You will own data models, storage, ingestion, transformations and orchestration, standardize data across real-world teleoperation and synthetic datasets, and collaborate with a team building general-purpose Physical AI.

Strong Python/Go, Spark, Kafka, Kubernetes, and Terraform experience required.

Qualifications

• 8+ years designing, building and maintaining large-scale data pipelines.
• Strong experience with distributed systems (Spark, Kafka).
• Experience with production-grade infrastructure (Kubernetes, Terraform).
• Proficient in Python or Go.

Responsibilities

• Design, build, and maintain large-scale data pipelines (batch and streaming).
• Own core data infrastructure: data model, storage, ingestion, transformation, orchestration.
• Standardize data models and unify pipelines across datasets (teleoperation and synthetic).
• Collaborate with a team building general-purpose Physical AI.

Skills

Python

Go

Data pipelines

Distributed systems

Production infra

Tools

Spark

Kafka

S3

Data lakes

Data warehouses

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