Data Infrastructure

Genesis Therapeutics
London, United Kingdom
Hybrid

Who this role is best for

Data infrastructure professionals with 8+ years of pipeline experience and distributed systems expertise will find this role in robotics and AI development at Genesis Therapeutics in London.

Best fit for

  • Candidates with 8+ years of experience in building production-grade data pipelines and a background in robotics or AI will align well with this role.
    — “Extensive experience designing, building, and maintaining large-scale data pipelines (8+ years)”
  • Individuals with a strong grasp of distributed systems and experience with Spark, Kafka, or similar technologies are well-suited for this position.
    — “Deep understanding of distributed systems (Spark, Kafka, or similar)”
  • Professionals who have standardized data models and unified pipelines across diverse datasets will stand out as a natural fit.
    — “Standardize data models and unify processing pipelines across real-world teleoperation and synthetic simulation datasets”

Things to consider

  • The role requires a high level of technical ownership over data infrastructure, including orchestration and transformation layers.
    — “Own core data infrastructure: data model, storage systems, ingestion pipelines, transformation frameworks, and orchestration layers”
  • Candidates should be prepared to work on a team focused on general-purpose Physical AI, which may involve interdisciplinary collaboration.
    — “Collaborate with a team of driven individuals committed to building general-purpose Physical AI”

How to stand out

  • Highlight experience with petabyte-scale data processing and AI systems, especially embodied AI like self-driving.
    — “Design, build, and maintain large-scale data pipelines (batch and streaming) for robotics foundation model training and evaluation at petabyte scale”
  • Showcase your hands-on experience with production-grade infrastructure tools like Kubernetes and Terraform.
    — “Experience running and maintaining production-grade infrastructure (Kubernetes, Terraform)”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design, build, and maintain large-scale data pipelines for robotics foundation model training and evaluation
  • 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
Typical background
8+ years of experience designing, building, and maintaining large-scale data pipelinesBachelor’s or Master’s degree in Computer Science, Engineering, or related field

Skills & requirements

Required

Large-scale Data PipelinesData Model DesignStorage SystemsIngestion PipelinesTransformation FrameworksOrchestration LayersDistributed SystemsData Storage TechnologiesProduction-grade InfrastructureAI Systems Support

Preferred

Embodied AISelf-driving Systems

Stack & domain

PythonGoData PipelinesDistributed SystemsSparkKafkaData Storage TechnologiesData LakesData WarehousesObject StoresS3Production-grade InfrastructureKubernetesTerraformAI SystemsEmbodied AISelf-drivingData Infrastructure

About the role

Original posting from Genesis Therapeutics via Ashby

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

Source: Genesis Therapeutics careers (Ashby)

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