Senior Cloud Infrastructure Engineer

Who we are Gatik, the leader in autonomous middle-mile logistics, is revolutionizing the B2B supply chain with its autonomous transportation-as-a-service (ATaaS) solution and prioritizing safe, consistent deliveries while streamlining freight movement by reducing congestion. The company focuses on short-haul, B2B logistics for Fortune 500 retailers and in 2021 launched the world’s first fully driverless commercial transportation service with Walmart. Gatik's Class 3-7 autonomous trucks are commercially deployed across major markets, including Texas, Arkansas, and Ontario, Canada, driving innovation in freight transportation. The company's proprietary Level 4 autonomous technology, Gatik Carrier™, is custom-built to transport freight safely and efficiently between pick-up and drop-off locations on the middle mile. With robust capabilities in both highway and urban environments, Gatik Carrier™ serves as an all-encompassing solution that integrates advanced software and hardware powering the fleet, facilitating effortless integration into customers' logistics operations. About the role We are seeking a Senior Cloud Infrastructure Engineer to architect and manage the large-scale compute and data infrastructure powering our autonomous driving stack. While researchers develop perception, planning, and world models, your mission is to build the high-performance systems and pipelines that make their work possible. You will be the backbone of our AI platform, ensuring that multi-GPU clusters, distributed training frameworks, and automated workflows are scalable, resilient, and cost-effective. This role is onsite 5 days a week at our Mountain View, CA office! What you'll do Cloud-Native Orchestration Kubernetes Advanced K8s Management: Architect and maintain mission-critical Kubernetes clusters optimized for heavy GPU/TPU workloads. GPU Scheduling: Implement and optimize Kubernetes-native GPU scheduling (NVIDIA GPU Operator) to ensure maximum hardware utilization. Infrastructure as Code: Drive the "Everything as Code" philosophy using Terraform, Helm, and cloud-native tools. Self-Healing Infrastructure: Deploy Autonomous AI Agents (LangGraph, CrewAI) to monitor cluster health and enable automated triage of hardware failures and NCCL timeouts. Data Engineering CI/CD Pipelines Autonomy Data Pipelines: Build large-scale pipelines using Apache Airflow, Kafka, and Spark to process raw sensor data into training-ready formats. GitOps: Implement robust GitOps workflows using ArgoCD, Gitlab CI/CD to automate the deployment of both infrastructure and model artifacts. Observability: Maintain deep visibility into infrastructure health and model serving performance using Prometheus, Grafana, and OpenTelemetry. Agentic DevOps CI/CD: Develop agent-driven workflows to optimize the developer experience, such as automated PR reviewers for Terraform and AI agents that proactively suggest Kubernetes resource-limit adjustments based on model training telemetry. Model Management Lifecycle (MLOps) Experiment Model Tracking: Design and maintain MLFlow and feature store integrations to provide a robust system of record for every model iteration. Workflow Automation: Build complex, automated model lifecycles using Airflow and Kubernetes to streamline the transition from training to simulation. High-Performance Serving: Support the deployment of models into simulation and production environments using Triton Inference Server, Ray Serve, and ONNX Runtime. Distributed Training ML Systems Support Training Systems Support: Enable researchers to scale models (VLA, World Models) across multi-node setups using PyTorch Distributed (TorchElastic), Ray Train, and Horovod. Networking Optimization: Optimize low-level communication (e.g., NCCL tuning, InfiniBand, or RoCE v2) to minimize latency for 3D Gaussian Splatting (3DGS) and large-scale training. Hardware-Aware Orchestration: Partner with researchers to fine-tune performance across multi-node GPU ... (truncated, view full listing at source)