Lead Software Engineer, Data Cloud

Salesforce Salesforce · Enterprise · Bellevue, WA

Lead Software Engineer role focused on architecting and building control plane services for Salesforce Data Cloud, orchestrating diverse compute workloads including AI/ML systems and GPU clusters. The role involves evolving compute infrastructure, designing intelligent job scheduling, and developing platform primitives for capacity management and workload isolation, with a focus on supporting AI/ML workloads at scale.

What you'd actually do

  1. Design and build our next gen Control Plane services
  2. Drive architectural decisions on resource isolation, multi-tenancy, and distributed system design
  3. Evolve compute infrastructure to support diverse workload patterns: batch processing, streaming, interactive queries, and AI/ML workloads at true petabyte scale
  4. Design intelligent job scheduling and orchestration systems to maximize resource utilization
  5. Build platform primitives for capacity management, priority-based allocation, and workload isolation

Skills

Required

  • 10+ years of software engineering experience
  • Go, Java, Python, or Rust
  • Kubernetes
  • distributed systems
  • containers
  • orchestration
  • networking
  • storage
  • compute isolation
  • control planes for distributed infrastructure

Nice to have

  • GPU infrastructure
  • ML/AI workload orchestration
  • serverless or function-as-a-service platforms
  • Prometheus
  • Grafana
  • OpenTelemetry
  • distributed tracing
  • Spark
  • Flink
  • Presto
  • vector databases
  • embedding infrastructure
  • open-source infrastructure projects
  • multi-tenant SaaS environments
  • AWS
  • GCP
  • Azure

What the JD emphasized

  • deep expertise in distributed systems
  • Expert-level Kubernetes experience
  • Production experience running large-scale infrastructure serving mission-critical workloads
  • Experience designing and building control planes for distributed infrastructure

Other signals

  • architect and build the next generation of our control plane services
  • orchestrate diverse compute workloads including GPU clusters, batch processing, real-time analytics, and AI/ML systems
  • Develop infrastructure strategies to sustain AI/ML workloads, focusing on GPU management and model deployment