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# Cloud Infrastructure Engineer

- **Company:** Upstart
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Bash Shell, Cloud Computing, Cloud Engineering, Computer Clusters, Continuous Integration, Information Engineering, DevOps, Distributed Computing Environment, Distributed Systems, Identity and Access Management, InfiniBand, Python (Programming Language), Role-Based Access Control, Prometheus, Pytorch, Grafana, Apache Spark, Gaussian, AI Platforms, Gitlab-ci, Kubernetes, Infrastructure Automation Frameworks, ONNX (Open Neural Network Exchange) Format, Apache Kafka, Machine Learning Operations, Virtual Agents, Terraform, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-cloud-infrastructure-engineer-gatik-ai-7872494

## About the Role

* 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., * 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.

## Description

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., + 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.


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