Senior Cloud Infrastructure Engineer

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 Santa Clara, 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 clusters for FSDP and DeepSpeed workloads.

What we're looking for

• Experience: 5+ years in Cloud Infrastructure, DevOps, or MLOps supporting high-scale compute environments.
• Kubernetes Mastery: Deep expertise in K8s, Helm, and container orchestration.
• Orchestration & Tooling: Strong background in Apache Airflow, Argo Workflows, MLFlow, and Terraform.
• Distributed Systems: Practical experience supporting frameworks like Ray and PyTorch Distributed.
• Core Skills: Proficiency in Python, Bash scripting, and a solid understanding of IAM/RBAC.

Bonus Qualifications

• Distributed Training Expertise: Deep understanding of FSDP, and DeepSpeed.
• AI Agent Orchestration: Experience building Agentic Workflows (LangGraph, AutoGen) for infrastructure automation or data curation.
• Advanced Protocols: Familiarity with Model Context Protocol (MCP) to connect AI agents with infrastructure tools.

Salary Range - $180,000- $240,000

More about Gatik

We have delivered complete, proprietary AV technology - an integration of software and hardware - to enable earlier successes for our clients in constrained Level 4 autonomy. By choosing the middle mile – with defined point-to-point delivery, we have simplified some of the more complex AV challenges, enabling us to achieve full autonomy ahead of competitors. Given extensive knowledge of Gatik’s well-defined, fixed route ODDs and hybrid architecture, we are able to hyper-optimize our models with exponentially less data, establish gate-keeping mechanisms to maintain explainability, and ensure continued safety of the system for unmanned operations.

Notable News

• Bloomberg: Autonomous Trucking Firm Gatik Inks Contracts Worth $600 Million
• Forbes: Hundreds’ Of Gatik Robot Delivery Trucks Headed For U.S. Roads
• Forbes:Gatik And Loblaw Announce Largest Commercial Deployment Of AV Trucks
• Forbes: Forget robotaxis. Upstart Gatik sees middle-mile deliveries as the path to profitable AVs
• Tech Brew: Gatik AI exec unpacks the regulations that could shape the AV industry
• Business Wire: Gatik Paves the Way for Safe Driverless Operations (‘Freight-Only’) at Scale with Industry-First Third-Party Safety Assessment Framework
• Auto Futures: Autonomous Trucking Group Gatik Secures Investment From NIPPON EXPRESS HOLDINGS
• Automotive News: Gatik foresees hundreds of self-driving trucks on road soon, and that's just the beginning
• Forbes: Isuzu And Gatik Go All In To Scale Up Driverless Freight Services
• Bloomberg: Autonomous Vehicle Startup Takes Off by Picking Off Easier Routes
• Reuters: Driverless vehicles on limited routes bump along despite US robotaxi scrutiny