5+ years in ML infrastructure, MLOps, or DevOps in high-scale environments
Deep understanding of multi-GPU training and high-performance networking
Mastery of Kubernetes, Terraform, and Helm for GPU orchestration
Experience with Agentic Workflows for automation
Expertise in MLFlow, Argo Workflows, and Kubernetes
Strong skills in Docker, Kubernetes, and Helm
Proficient in Python and Bash; Go or Rust experience is a plus
Responsibilities
Enable researchers to scale complex models across multi-node setups using PyTorch Distributed and Ray Train
Architect and optimize multi-GPU setups for efficient model and data parallelism
Optimize low-level network communication to reduce latency during training
Improve hardware utilization and cost-effectiveness with Kubernetes-native GPU scheduling
Deploy and scale optimized model artifacts for real-time and batch processing
Design autonomous AI agents for monitoring GPU health and automated triaging
Automate the end-to-end model lifecycle with ML infrastructure management
Benefits
Onsite work environment in Mountain View, CA, 5 days a week
Commitment to diversity and inclusion within the team
Opportunities for professional growth and collaboration
Contributions to resilient supply chains and environmental sustainability
Participation in a pioneering company tackling autonomous logistics challenges
Full Job Description
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: Collaborate with data engineering teams to scale high-bandwidth ETL pipelines using Apache Airflow, Kafka, and Spark, ensuring seamless data flow from raw sensor logs to optimized storage in S3, GCS, or Delta Lake
Monitoring & Observability
System Metrics: Define and track key ML system metrics, including training convergence, latency, throughput, and drift detection.
Infrastructure Health: Maintain deep visibility into platform health using Prometheus, Grafana, OpenTelemetry, and ELK Stack.
Deep Stack Observability: Develop comprehensive monitoring using Prometheus, Grafana, and OpenTelemetry to track low-level infrastructure health alongside high-level ML metrics like training convergence and throughput.
AI-Specific Metrics & Drift: Define and monitor critical ML system KPIs, including model latency, inference throughput, and feature drift detection
What we're looking for
Experience: 5+ years in ML infrastructure, MLOps, or DevOps supporting high-scale compute environments.
ML Expertise: Deep understanding of multi-GPU training strategies (FSDP, DeepSpeed, Ray Train) and high-performance networking (NCCL, InfiniBand).
Infrastructure Automation: Mastery of Kubernetes, Terraform, and Helm, with a focus on GPU-native orchestration.
AI Agent Frameworks: Proven experience building or supporting Agentic Workflows for infrastructure or data automation (e.g., using LLMs to drive DevOps tasks).
Platform Mastery: Expertise in MLFlow, Argo Workflows, and Kubernetes.
Containerization: Strong experience with Docker, Kubernetes, and Helm.
Data & CI/CD: Proficiency in Apache Airflow, Kafka, Spark, and GitOps automation.
Core Skills: Proficiency in Python and Bash; experience with Go or Rust is a plus
Bonus Qualifications
Advanced AI Protocols: Familiarity with the Model Context Protocol (MCP) to standardize how AI agents interact with internal databases and orchestration APIs.
Hybrid & Physical AI: Experience in hybrid cloud and on-prem GPU cluster management for Physical AI workloads (e.g., 3DGS, World Models).
Agentic Observability: Experience utilizing LLMs for semantic monitoring and log analysis to detect complex distributed system failures that traditional threshold-based alerts miss.
Salary Ranges - $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.
