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Senior Ai Infrastructure Engineer Jobs (NOW HIRING)

Together AI

AI Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

As an AI Infrastructure Engineer, you will be responsible for maintaining user-facing services and production systems, implementing best practices for availability and scalability, and building ...

Together AI

AI Infrastructure Engineer

San Francisco, CA · On-site

$190K - $270K/yr

As an AI Infrastructure Engineer at Together, you are responsible for keeping all user-facing services and production systems running smoothly. You are a blend of a pragmatic operator and a software ...

STN Incorporated

AI Infrastructure Engineer

Pleasanton, CA · Remote

$145K - $195K/yr

Overview The AI Infrastructure Engineer will be responsible for designing, deploying, and maintaining robust infrastructure systems tailored for AI and machine learning operations. This role focuses ...

Nvidia

OR

$108K - $147K/yr

NVIDIA is looking for an outstanding, passionate, and dedicated Senior AI Infrastructure Engineer to join our DGX Cloud group. This engineering role will design, build and maintain large-scale ...

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · On-site +1

$170K - $210K/yr

The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves Utilidata\'s AI and ML models across edge deployments, cloud environments ...

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · Remote

$170K - $210K/yr

The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves Utilidata's AI and ML models across edge deployments, cloud environments ...

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 ...

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How much do senior ai infrastructure engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for senior ai infrastructure engineer in the United States is $126,969.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,500.00 and $147,500.00 per year, depending on experience, location, and employer.

What does a Senior AI Infrastructure Engineer do?

A Senior AI Infrastructure Engineer is responsible for designing, building, and maintaining the large-scale computing systems that support artificial intelligence (AI) and machine learning (ML) workloads. They work on optimizing data pipelines, managing cloud or on-premise infrastructure, ensuring scalability, and enabling efficient training and deployment of AI models. These professionals collaborate closely with data scientists, software engineers, and IT teams to create robust, high-performance environments that support the rapid development and deployment of AI solutions.

What are some typical challenges faced by Senior AI Infrastructure Engineers when scaling AI systems for production?

Senior AI Infrastructure Engineers often encounter challenges related to managing large-scale data pipelines, ensuring low-latency model serving, and maintaining system reliability as user demand grows. Balancing resource allocation for compute-intensive workloads, optimizing infrastructure costs, and implementing robust monitoring are common hurdles. Collaboration with data scientists, DevOps, and product teams is crucial to streamline deployment cycles and rapidly address issues as they arise. Mastery of distributed systems and cloud platforms often distinguishes top performers in this role.

What are the key skills and qualifications needed to thrive as a Senior AI Infrastructure Engineer, and why are they important?

To thrive as a Senior AI Infrastructure Engineer, you need deep expertise in computer science, cloud computing, distributed systems, and AI/ML frameworks, often supported by a relevant degree and significant experience. Proficiency with tools such as Kubernetes, Docker, TensorFlow, PyTorch, and cloud platforms like AWS or Azure—as well as experience with CI/CD pipelines—is typically required. Strong problem-solving abilities, collaboration, and effective communication are standout soft skills for this role. These competencies are crucial for designing scalable, reliable AI infrastructure that supports complex machine learning workflows and organizational goals.
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Senior Ai Infrastructure Engineer jobs near you

Senior AI Infrastructure Engineer - Computer Vision

Obvio

San Carlos, CA • On-site

$131K - $178K/yr

Full-time

Posted 27 days ago

Job description

Job Summary:
Obvio is a company focused on preventing traffic-related deaths through innovative AI technology. They are seeking a Senior AI Infrastructure Engineer to build and optimize the core ML infrastructure layer, including orchestration, compute, and data management systems.
Responsibilities:
• Build the orchestration layer.
• Design and implement a scalable workflow system to ingest, route, and process incoming events.
• Define the stages of the pipeline — ingestion, preprocessing, inference, validation, and delivery — and build something that handles failures gracefully at high throughput.
• Scale the inference fleet.
• Build the compute layer that parallelizes processing across the event backlog and handles burst capacity as our camera fleet grows.
• Design the worker pool, queueing, and autoscaling strategy for GPU-bound workloads on ECS.
• Design the data plumbing.
• Own the path from edge device to pipeline output — storage, metadata, and the triggers that drive processing.
• Build something that is observable, debuggable, and auditable end-to-end.
• Build the model serving and lifecycle layer.
• Stand up the infrastructure that loads versioned CV models and handles inference reliably.
• Optimize for GPU utilization and throughput where it matters — dynamic batching, multi-model serving, and model optimizations like quantization or TensorRT/ONNX.
• Ensure new model versions can be promoted and rolled back without pipeline downtime.
• Set the engineering standard.
• Write the playbooks — runbooks, deployment procedures, testing standards — that the team builds on as we grow.
Qualifications:
Required:
• 6+ years building and operating production backend or data-intensive systems at scale, with meaningful experience working on ML-heavy pipelines.
• You've owned something through its full lifecycle — design, deployment, scaling, and on-call — and you've done it in a context where ML inference was a first-class part of the system.
• You've used a workflow orchestration tool to build production pipelines, not just evaluate them.
• Comfortable with the building blocks — compute, queues, storage, networking — and you think in terms of cost, reliability, and operational simplicity rather than just what works.
• You've built or operated pipelines where ML inference is a core stage, and you understand what those workloads need — throughput constraints, GPU economics, model versioning, and keeping model performance visible in production.
• You don't need to have trained the models, but you know how to run them reliably at scale.
• You don't reach for the first framework you know. You understand the problem, evaluate tradeoffs honestly, and build something that fits the actual scale and constraints.
Preferred:
• Experience with CV or video pipelines is a plus.
Company:
Obvio provides AI-powered traffic safety solutions using solar-powered monitoring cameras. Founded in 2023, the company is headquartered in San Carlos, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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