5+ years experience in Cloud Infrastructure, DevOps, or MLOps in high-scale environments
Expert level proficiency in Kubernetes, Helm, and container orchestration
Strong experience with Apache Airflow, Argo Workflows, MLFlow, and Terraform
Hands-on experience with distributed systems like Ray and PyTorch Distributed
Proficient in Python and Bash scripting, with knowledge of IAM/RBAC concepts
Responsibilities
Architect and manage Kubernetes clusters for GPU/TPU workloads
Implement GPU scheduling using NVIDIA GPU Operator
Utilize Infrastructure as Code tools like Terraform and Helm
Deploy AI agents for monitoring cluster health and automated triage
Build large-scale data pipelines with Apache Airflow, Kafka, and Spark
Automate deployments using GitOps workflows with ArgoCD and Gitlab CI/CD
Support MLOps by designing model tracking systems with MLFlow
Benefits
Health, dental, and vision insurance
Flexible work hours
401(k) plan with company match
Professional development opportunities
Collaborative work environment
Full Job Description
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
About Gatik AI
Gatik AI is a technology company that develops autonomous vehicles for business to business short-haul logistics. The company was founded in 2017 by Gautam Narang and Arjun Narang. Gatik AI's vehicles are designed to operate on fixed routes between distribution centers, warehouses, and retail locations. The company's mission is to deliver goods safely, efficiently, and on time, while reducing road congestion and carbon emissions. Gatik AI has partnerships with Walmart and Loblaw Companies Limited, two of the largest retailers in North America. The company is headquartered in Vancouver, Canada, with offices in Palo Alto, California.
