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Ai Infrastructure Jobs (NOW HIRING)

Gatik AI

Senior AI Infrastructure Engineer

Santa Clara, CA · On-site

$127K - $173K/yr

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 ...

MeshyAI

AI Infrastructure Engineer

Sunnyvale, CA · On-site

$125K - $164K/yr

The AI Infrastructure Engineer role involves ensuring the reliability and scalability of the AI model serving stack, while developing core engineering infrastructure to connect models to product ...

Analogdevices

Senior AI Infrastructure Engineer

Wilmington, MA · Hybrid

$118K - $161K/yr

Senior AI Infrastructure Engineer, Developer Experience Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the ...

Seekr

Senior AI Infrastructure Engineer

Austin, TX

$107K - $146K/yr

As a Senior AI Infrastructure Engineer, you will design, build, and operate the platforms that enable large-scale training, serving, evaluation, and deployment of foundation models and autonomous AI ...

Reveille Technologies

AI Infrastructure Engineer

Austin, TX · On-site

$106K - $139K/yr

Hi, Job Title : AI Infrastructure Engineer Location : Austin, TX or Fort mill, SC Duration: Fulltime or Contract Candidates must demonstrate strong hands-on expertise in Python, live coding ...

Outsourceit

AI Infrastructure Lead

San Francisco, CA · On-site

$120 - $170/hr

We need an AI Infrastructure Lead to own the design and operation of our GPU cluster management layer, model serving pipeline, and low-latency routing system. You will work directly with the CTO and ...

Seekr

Senior AI Infrastructure Engineer

Austin, TX · On-site

$107K - $146K/yr

As a Senior AI Infrastructure Engineer, you will design, build, and operate the platforms that enable large-scale training, serving, evaluation, and deployment of foundation models and autonomous AI ...

vCluster Labs

AI Infrastructure Engineer

New York, NY · Remote

$150K - $200K/yr

As vCluster's AI Infrastructure Specialist, you will work directly with customers at the earliest and most critical stage of their journey: from bare metal GPU nodes through to a production-ready ...

Autonomous Solutions

AI Infrastructure Engineer IV

Lehi, UT · On-site

$100K - $132K/yr

As an AI Infrastructure Engineer IV , you will play a critical role in designing, building, and maintaining the systems that power our AI and machine learning capabilities. You will ensure our ...

Autonomous Solutions

AI Infrastructure Engineer IV

Mendon, UT · On-site

$93K - $122K/yr

As an AI Infrastructure Engineer IV, you will play a critical role in designing, building, and maintaining the systems that power our AI and machine learning capabilities. You will ensure our compute ...

Autonomous Solutions

AI Infrastructure Engineer IV

Lehi, UT · On-site

$100K - $132K/yr

As an AI Infrastructure Engineer IV , you will play a critical role in designing, building, and maintaining the systems that power our AI and machine learning capabilities. You will ensure our ...

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Ai Infrastructure information

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How much do ai infrastructure jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for ai infrastructure in the United States is $59.18, according to ZipRecruiter salary data. Most workers in this role earn between $48.08 and $68.99 per hour, depending on experience, location, and employer.

What is the difference between Ai Infrastructure vs Data Engineer?

AspectAi InfrastructureData Engineer
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of cloud platforms and AI toolsBachelor's in CS, Data Science, or related; programming and database skills
Work EnvironmentCloud environments, AI model deployment, infrastructure setupData pipelines, database management, data processing
Employer & Industry UsageTech companies, AI startups, cloud providersTech firms, finance, healthcare, e-commerce

Ai Infrastructure professionals focus on building and maintaining the hardware and software systems that support AI models, while Data Engineers develop and manage data pipelines and databases. Both roles require technical skills and often collaborate but serve different core functions within AI and data ecosystems.

How much do AI infrastructure engineers make?

AI infrastructure engineers typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and hardware may earn higher salaries, often exceeding $180,000.

What are AI infrastructure jobs?

AI infrastructure jobs involve designing, building, and maintaining the hardware, software, and network systems necessary to support artificial intelligence applications. These roles often require knowledge of cloud computing, data centers, machine learning frameworks, and system optimization to ensure reliable and efficient AI model deployment and operation.

What are the key skills and qualifications needed to thrive in AI infrastructure?

To thrive in AI Infrastructure, you need expertise in software engineering, distributed systems, cloud platforms, and a solid understanding of machine learning workflows, often supported by degrees in computer science or related fields. Familiarity with tools like Kubernetes, Docker, Terraform, and cloud services (AWS, GCP, Azure), as well as experience with CI/CD pipelines and monitoring systems, is essential. Strong problem-solving abilities, effective communication, and adaptability help professionals excel in cross-functional teams and rapidly evolving environments. These skills and qualities are crucial for building scalable, reliable systems that power AI applications and support organizational innovation.

What are common challenges faced by professionals working in AI infrastructure roles, and how can they be addressed?

Professionals in AI Infrastructure roles often encounter challenges related to scalability, system reliability, and integration with existing IT environments. Managing rapidly growing datasets and ensuring seamless deployment of machine learning models can be complex, requiring robust automation and monitoring tools. Collaboration with data scientists, software engineers, and DevOps teams is critical to ensure infrastructure meets the evolving needs of AI projects. Staying updated with the latest cloud technologies and best practices can help address these challenges and drive successful AI implementations.

What is AI infrastructure?

AI infrastructure refers to the combination of hardware, software, and cloud-based solutions that support the development, deployment, and scaling of artificial intelligence applications. It includes components such as GPUs, CPUs, storage systems, networking, data management tools, and machine learning frameworks. The goal of AI infrastructure is to provide the computational power and resources needed to train, test, and run AI models efficiently, whether on-premises or in the cloud. Organizations invest in robust AI infrastructure to accelerate innovation, manage large datasets, and ensure the reliability of their AI systems.
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Ai Infrastructure jobs near you
Infographic showing various Ai Infrastructure job openings in the United States as of August 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $123,103 per year, or $59.2 per hour.

Senior AI Infrastructure Engineer

Gatik AI

Santa Clara, CA • On-site

$127K - $173K/yr

Full-time

Re-posted 24 days ago

Job description

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 Santa Clara, 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
More about Gatik
Founded in 2017 by experts in autonomous vehicle technology, Gatik has rapidly expanded its presence to Mountain View, Dallas-Fort Worth, Arkansas, and Toronto. As the first and only company to achieve fully driverless middle-mile commercial deliveries, Gatik holds a unique and defensible position in the AV industry, with a clear trajectory toward sustainable growth and profitability.
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.
Visit us at Gatik for more company information and Careers at Gatik for more open roles.
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
Taking care of our team
At Gatik, we connect people of extraordinary talent and experience to an opportunity to create a more resilient supply chain and contribute to our environment's sustainability. We are diverse in our backgrounds and perspectives yet united by a bold vision and shared commitment to our values. Our culture emphasizes the importance of collaboration, respect and agility.
We at Gatik strive to create a diverse and inclusive environment where everyone feels they have opportunities to succeed and grow because we know that together we can do great things. We are committed to an inclusive and diverse team. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.

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