Point Wild is hiring a Principal Platform Engineer

We’re looking for a Principal Platform Engineer to architect and lead the infrastructure strategy for our next-generation Production ML platform on Google Cloud. In this role, you will be the backbone of our high-performance machine learning workloads, ensuring our systems are elastic, secure, and resilient. You won’t just maintain the status quo; you’ll build the "paved road" for our engineers, automating everything from model deployment to complex networking perimeters. We are a high-trust, outcome-focused team that moves quickly to solve some of the most challenging problems in the ML space. Core Responsibilities: - Infrastructure Management: Design, deploy, and maintain elastic scaling cloud infrastructure (GCP) and containerization tools like Kubernetes for high-performance ML workloads. - CI/CD Pipeline Development and maintenance: Build automated pipelines for training, testing, and deploying machine learning models using tools like Jenkins, GitHub Actions, or Airflow. - Model Monitoring & Maintenance: Implement observability tools to track model drift, accuracy, latency, and performance degradation in production. - Collaboration: Bridge the gap between data engineers, ML engineers, Backend and Frontend engineers to ensure smooth production operation. - ML Observability: Implement comprehensive monitoring for system health (latency/uptime) alongside ML-specific metrics, such as feature drift, prediction accuracy, and data distribution shifts, to ensure long-term model reliability. Non ML workload and production metrics monitoring. - Deploy tools that empower individual teams to monitor their workloads. - Participate in on-call rotation, help manage posture to ensure compliance with standards such as SOC. What you bring to the table: - Senior Expertise: 8 - 10+ years in DevOps/Platform Engineering, with at least 2 years of experience specifically operating and maintaining production ML workloads. - GCP & K8s Mastery: Deep, hands-on experience with GCP (VPC-SC, IAM, Organization Policies) and GKE (Cluster topology, Helm, Kustomize, and in-cluster operators like ArgoCD). - Service Mesh Excellence: High proficiency with Istio (VirtualServices, mTLS, sidecar injection) and API Gateways (specifically Kong). - Infrastructure as Code: Expert-level Terraform skills, specifically using an Atlantis/GitOps workflow across a massive, multi-hundred-file estate. - Secrets & Identity: Experience managing enterprise-grade identity and secrets (Auth0, Dex, ESO, or SOPS). - Data/ML Tooling: Experience operating Airflow in production and an ML-serving stack (e.g., Triton, vLLM, MLflow). - Database Management: Comfortable managing Cloud SQL (PostgreSQL), BigQuery, and in-cluster datastores like Elasticsearch or ClickHouse. - At least an upper-intermediate level of spoken and written English. It would be great if you also had: - ML Observability: Past experience with continuous monitoring of model accuracy and detecting data/concept drift. - Automation Savvy: Experience with Ansible for cluster bootstrap and recovery. - Advanced Certifications: Kubernetes (CKA/CKS) or GCP Professional Cloud Architect/Security Engineer certifications. - Modern Stack Exposure: Familiarity with Loki, Grafana, or managing ClickHouse at scale.