Senior AI 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 AI Infrastructure Engineer to design, build, and scale the high-performance AI platform powering our autonomous driving models. While researchers focus on developing perception, planning, and world models, you will be responsible for the underlying infrastructure that enables distributed training, experiment tracking, and seamless model deployment. You will bridge the gap between research and production, ensuring our AI stack is scalable, resilient, and highly efficient This role is onsite 5 days a week at our Mountain View, CA office! What you'll do Distributed Training ML Systems Support Scale Research Workloads: Enable researchers to scale complex models (VLA, World Models) across multi-node setups using PyTorch Distributed, and Ray Train. Performance Optimization: Architect and optimize multi-GPU setups, ensuring efficient model parallelism and data parallelism techniques across H100/A100 clusters. Networking Hardware Tuning: Optimize low-level communication (e.g., NCCL tuning, InfiniBand, or RoCE v2) to minimize latency for 3D Gaussian Splatting (3DGS) and large-scale training. Intelligent Resource Scheduling: Optimize hardware utilization and cost-efficiency through Kubernetes-native GPU scheduling (NVIDIA GPU Operator, KubeFlow). Inference Performance Engineering: Deploy and scale optimized model artifacts using TensorRT, ONNX Runtime, and Triton Inference Server, fine-tuning pipelines for both real-time and batch processing Agentic Infrastructure Automation Self-Healing AI Infrastructure: Architect and deploy Autonomous AI Agents (LangGraph, CrewAI, or AutoGen) to monitor GPU cluster health, enabling automated real-time triage of hardware failures and NCCL timeouts. Agentic DevOps CI/CD: Develop agent-driven automation, such as Agentic PR Reviewers for infrastructure code and AI agents that proactively suggest model-specific Kubernetes resource optimizations. Agentic Data Curation: Support researchers in building "Data Machines" where AI agents autonomously curate, label, and verify high-priority edge cases from raw data. Model Management Lifecycle (MLOps) Automated Lifecycle Management: Design and maintain ML infrastructure leveraging MLFlow, Argo Workflows, and Kubernetes to automate the end-to-end model lifecycle. Experiment Model Tracking: Integrate feature stores and experiment tracking systems to provide a robust system of record for every model iteration. Deployment Strategies: Implement robust serving mechanisms, including A/B testing, shadow deployments, and rollback mechanisms Cloud-Native Foundations Data Integration Infrastructure as Code: Drive the "Everything as Code" philosophy using Terraform and Helm. Data Pipelines: Collaborate with data teams to scale ETL pipelines using Apache Airflow, Kafka, and Spark for large-scale dataset management. ○ Integrated Data Factories: Collabo ... (truncated, view full listing at source)