Platform Engineer

• You’ll own the infrastructure that runs our deploy anywhere platform — from Kubernetes clusters serving ML inference at scale to the CI/CD pipelines, IaC, and observability stack that keep it all reliable
• This is a hands-on role: you’ll write Helm charts and Terraform one day, debug a Kafka consumer lag issue the next, and ship a backend service feature the day after
• You’ll deploy across AWS, GCP, and on-premises (including air-gapped environments), and you’ll participate in an on-call rotation supporting enterprise customers
• Build and maintain infrastructure-as-code (Terraform, Helm) for our AWS EKS and GCP GKE clusters, plus on-premises deployments (including Tanzu and air-gapped environments)
• Own CI/CD pipelines (GitHub Actions, Bazel, ArgoCD) and drive GitOps adoption
• Deploy, scale, and optimize ML/NLP inference workloads (vLLM, PyTorch, GPU scheduling with various Kubernetes scalers)
• Build and improve observability: Prometheus, Grafana, Datadog,, and OpenTelemetry
• Collaborate with Field Engineering to support PoCs and platform deployments in customer cloud VPCs and on-prem environments
• Contribute to backend services (Java 21, Python, gRPC) and platform features
• Improve system reliability, scalability, and developer experience across the engineering org

Benefits

• Comprehensive medical insurance
• Life & disability insurance
• Employee assistance program
• Free gym access at HQ
• Annual global summit
• Free snacks and beverages
• Bereavement leave
• Monthly stipend for phone/internet
• Maternity/Paternity leave
• Minimal meeting Fridays
• Flexible working hours
• Unlimited vacation time policy
• Home office stipend for remote employees

• Solid fundamentals in Linux, networking, and distributed systems
• Hands-on with infrastructure-as-code: Terraform, Helm, or Pulumi
• Experience with at least one major cloud provider (AWS preferred; GCP or Azure also valued)
• Working knowledge of CI/CD systems (GitHub Actions, Bazel, ArgoCD, or similar)
• Proficiency in one or more of: Go, Python, Java. Comfortable reading and contributing to backend codebases
• Strong Kubernetes experience (deployment, debugging, scaling — not just kubectl apply)
• 2+ years in platform engineering, DevOps, SRE, or backend infrastructure roles
• Experience deploying or operating ML inference workloads (model serving, GPU scheduling, vLLM, TensorFlow Serving, or similar)
• Familiarity with streaming/messaging systems (Kafka, Pulsar) and data stores (MariaDB/PostgreSQL, Aerospike, ClickHouse, OpenSearch)
• Experience with GitOps workflows (ArgoCD, Flux)
• Exposure to air-gapped or on-premises Kubernetes deployments
• Background in observability tooling (Prometheus, Grafana, OpenTelemetry, Datadog)
• Experience providing technical support or working directly with enterprise customers on infrastructure issues
• Comfort with AI-assisted development workflows and managing AI coding agents