Software Engineer II – Insights / Platform Analytics (backend / Infrastructure)

UiPath UiPath · Enterprise · Bellevue, WA · Engineering

Software Engineer II role focused on building backend systems and distributed services for UiPath's cloud platform, specifically for Insights/Platform Analytics. The role involves developing infrastructure for observability, analytics, and insights, with a focus on integrating AI-driven capabilities into real-world workflows and applying AI to engineering challenges. Experience with Kubernetes, cloud infrastructure, and backend development is required, with a preference for experience in AI/ML application to engineering problems.

What you'd actually do

  1. Design and build backend systems and distributed services that power large-scale cloud infrastructure
  2. Develop automation for provisioning, deployment, and lifecycle management of cloud resources
  3. Build and operate systems that enable platform-level analytics, observability, and insights at scale
  4. Contribute to Kubernetes-based infrastructure, including operators, controllers, and platform services
  5. Work with or grow into data platform integrations (e.g., warehouses, pipelines, Snowflake) to support platform intelligence

Skills

Required

  • 4+ years of software engineering experience building backend or distributed systems
  • Strong fundamentals in data structures, algorithms, and system design
  • Experience working with Kubernetes (K8s), containers, and cloud-native systems
  • Experience with cloud infrastructure (Azure preferred, AWS or GCP acceptable)
  • Strong proficiency in C# (primary), with additional experience in Python and/or Scala
  • Experience with infrastructure as code (e.g., Terraform)
  • Demonstrated ability to own production systems, debug issues, and drive them to resolution

Nice to have

  • Experience building or operating large-scale distributed systems or control planes
  • Exposure to data platforms (e.g., Snowflake, data warehouses, ETL pipelines) or strong desire to ramp quickly
  • Familiarity with CNCF ecosystem tools (e.g., Istio, Prometheus)
  • Experience building platform primitives (operators, controllers, orchestration layers)
  • Curiosity or hands-on experience applying AI/ML to engineering problems (e.g., automation, observability, developer productivity)

What the JD emphasized

  • AI-driven capabilities into real-world workflows
  • applying AI to real engineering and platform challenges
  • own production systems
  • curiosity or hands-on experience applying AI/ML to engineering problems

Other signals

  • building systems that enable deep visibility, intelligence, and data-driven decision-making at scale
  • increasingly integrates AI-driven capabilities into real-world workflows
  • applying AI to real engineering and platform challenges