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Ml Infrastructure Engineer Jobs (NOW HIRING)

Zensors

AI/ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

As a Machine Learning Engineer in ML Runtime & Optimization , you will develop technologies to ... infrastructure. * Model Acceleration: Applying advanced model optimization techniques--such as ...

Spellbrush

HPC/ML Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

Spellbrush is seeking an experienced HPC/ML Infrastructure Engineer to lead the administration and operations of a large anime AI training cluster. The role involves bridging the gap between ...

General Motors

ML Infrastructure Engineer

Sunnyvale, CA · Hybrid

$119K - $187K/yr

We are looking for a Software Engineer to join our team and help us scale our platform for ... Experience infrastructure applications or similar experience Compensation: The compensation ...

General Motors

ML Infrastructure Engineer

Sunnyvale, CA · On-site

$119 - $188/hr

We are looking for a Software Engineer to join our team and help us scale our platform for ... Experience infrastructure applications or similar experience Compensation The compensation ...

General Motors

$119 - $188/hr

We are looking for a Software Engineer to join our team and help us scale our platform for ... Experience infrastructure applications or similar experience Compensation The compensation ...

Arlo

Senior ML Infrastructure Engineer

New York, NY · On-site

$180K - $230K/yr

We're hiring a Senior ML Infrastructure Engineer to build and own the infrastructure that powers it -- from training models on tens of millions of patients and hundreds of millions of rows of claims ...

General Motors

ML Infrastructure Engineer

Sunnyvale, CA · On-site

$119K - $187K/yr

We are looking for a Software Engineer to join our team and help us scale our platform for ... Experience infrastructure applications or similar experience Compensation: The compensation ...

Arlo

Senior ML Infrastructure Engineer

New York, NY · On-site

$180K - $230K/yr

We're hiring a Senior ML Infrastructure Engineer to build and own the infrastructure that powers it - from training models on tens of millions of patients and hundreds of millions of rows of claims ...

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$124K - $250K/yr

A Day in the Life As a member of our software engineering infra team, you'll solve technical challenges, including upgrading and implementing state-of-the-art software infrastructure. The team builds ...

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

A Day in the Life As a member of our software engineering infra team, you'll solve technical challenges, including upgrading and implementing state-of-the-art software infrastructure. The team builds ...

General Motors

$155 - $206/hr

We are seeking a Senior ML Infrastructure engineer to help build and scale robust Compute platforms for Simulation workflows. In this role, you will focus on scaling, driving efficiency, and high ...

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All JobsMl Infrastructure Engineer Jobs

Ml Infrastructure Engineer information

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$46.5K

$127.1K

$182K

How much do ml infrastructure engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for ml infrastructure engineer in the United States is $127,066.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $141,000.00 per year, depending on experience, location, and employer.

What is the difference between Ml Infrastructure Engineer vs Data Engineer?

AspectML Infrastructure EngineerData Engineer
Required CredentialsBachelor's/Master's in CS, experience with cloud platforms, scripting, and ML toolsBachelor's/Master's in CS, experience with databases, ETL, and data pipelines
Work EnvironmentFocus on deploying and maintaining ML systems, cloud infrastructure, and automationDesigning and building data pipelines, managing large datasets, and data storage
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and data-driven industries

The ML Infrastructure Engineer specializes in building and maintaining the infrastructure that supports machine learning models, focusing on deployment, scalability, and automation. In contrast, Data Engineers primarily develop data pipelines and manage large datasets to enable data analysis and business intelligence. Both roles require strong technical skills and often overlap, but their core focus areas differ significantly.

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Ml Infrastructure Engineer jobs near you
Infographic showing various Ml Infrastructure Engineer job openings in the United States as of August 2026, with employment types broken down into 93% Full Time, 3% Part Time, and 4% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.

AI/ML Infrastructure Engineer

Zensors

San Francisco, CA • On-site

$126K - $166K/yr

Full-time

Re-posted 24 days ago

Job description

The AI Infrastructure team at Zensors builds the engine that powers our visual sensing platform. We provide the tools to automate the lifecycle of our AI workflow, including model development, evaluation, optimization, deployment, and monitoring across thousands of video streams.

As a Machine Learning Engineer in ML Runtime & Optimization, you will develop technologies to accelerate the training and inference of computer vision models that power smart spaces and cities.

Your responsibilities will include:

  • Optimizing Core ML Pipelines: Identifying key bottlenecks in our current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.

  • Cross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of our next-generation inference infrastructure.

  • Model Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to our Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.

  • Building Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.

  • Resource Efficiency: Reducing the compute cost per video stream to enable massive scalability of our SaaS product.

  • Data Management: Building, improving, maintaining, and operating systems to facilitate the collection, labeling, and use of visual data for ML training.

Requirements

  • BS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.

  • Strong programming skills in C/C++ and Python.

  • Experience with model optimization, quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).

  • Deep understanding of GPU hardware performance, including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.

  • Experience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.

  • Experience identifying and resolving compute and data flow bottlenecks, particularly in high-bandwidth video processing pipelines.

  • Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.

Preferred Qualifications

  • Familiarity with database systems (e.g., SQL, Neo4j).

  • Work in Computer Vision, Deep Learning, and Vision Transformers.

  • Experience with video processing frameworks such as NVIDIA DeepStream, DALI, or FFmpeg.

  • Familiarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.

  • Knowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).