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Ml Infrastructure Engineer Jobs (NOW HIRING)

Gridware

ML Infrastructure Engineer

San Francisco, CA · On-site

$190K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

Gridware

ML Infrastructure Engineer

San Francisco, CA

$245K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

Gridware

ML Infrastructure Engineer

San Francisco, CA · On-site

$190K - $260K/yr

Role Description As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure ...

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

They are seeking an ML Infrastructure Engineer to design, develop, and maintain large-scale distributed systems while collaborating with various engineering teams to enhance their infrastructure and ...

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

The ML Infrastructure Engineer will design, develop, and maintain large-scale distributed systems while collaborating with various engineering teams to enhance the company's technology stack.

AppLovin

ML Infrastructure Engineer

Palo Alto, CA · On-site

$126K - $165K/yr

The ML Infrastructure Engineer will be responsible for designing, developing, and maintaining large-scale distributed systems, collaborating with engineering teams, and optimizing the model delivery ...

Sunday Inc

ML Infrastructure Engineer

Redwood City, CA · On-site

$131K - $172K/yr

Strong software engineering and systems fundamentals * Experience building distributed systems or large-scale data pipelines * Hands-on experience with ML training infrastructure, ideally PyTorch

Rebar

Senior ML Infrastructure Engineer

New York, NY · On-site

$118K - $161K/yr

We're looking for a Senior ML Infrastructure Engineer to build the platform our ML engineers depend on to rapidly iterate, experiment, and ship models - spanning feature pipelines, training ...

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 ...

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 ...

Sygaldry

ML Infrastructure Engineer

San Francisco, CA · On-site

$220K - $250K/yr

Coordinate with our infrastructure engineer on cloud administration and security Pipeline ... Python-based ML and scientific computing tooling (PyTorch, JAX) * GCP and/or Modal experience

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

$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 ...

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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 Jun 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.

More about Ml Infrastructure Engineer jobs
What cities are hiring for Ml Infrastructure Engineer jobs? Cities with the most Ml Infrastructure Engineer job openings:
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Ml Infrastructure Engineer jobs near you
Infographic showing various Ml Infrastructure Engineer job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 33% In-person, and 67% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.
ML Infrastructure Engineer

ML Infrastructure Engineer

Mach9 Robotics Inc

San Francisco, CA • On-site

$160K - $200K/yr

Full-time

Posted 12 days ago

Job description

The role
At Mach9, ML infrastructure engineers build and maintain the systems that power production AI models for civil engineering and surveying. Our ML pipeline spans 10,000+ miles of labeled survey data, image segmentation networks, and 3D prediction models serving real-time inference to surveyors and engineers in the field.
This role is ideal for mid-career ML infrastructure engineers with experience building for both training and inference.
You'll build training pipelines that handle deep transformer models on hundreds of terabytes of 3D point cloud and image data. You'll also architect our inference infrastructure, delivering both heavy offline detection algorithms and real-time responsive inference that integrates directly with our CAD software.
Responsibilities
  • Design and build a centralized system for versioning training data, generated datasets, and model artifacts, with full lineage tracking from raw source data through to trained model outputs.
  • Develop and maintain reliable, reproducible ML training and data generation pipelines.
  • Refactor and harden existing training and data generation scripts into composable, testable, and maintainable components.
  • Create CI/CD workflows for validating data pipelines and model training runs, including automated correctness checks and regression detection.
  • Build tooling that enables ML engineers to launch, monitor, and debug training jobs with minimal friction.
  • Optimize and scale real-time model inference services to meet latency and throughput requirements in production, including profiling, batching strategies, and resource-efficient serving.
  • Own the deployment path from trained model artifact to production endpoint, ensuring reliable rollouts, rollback, and monitoring.

Requirements
  • 3+ years of work experience in relevant fields.
  • Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience.
  • Strong communication skills and the ability to work closely with ML researchers and engineers to understand their workflows and translate them into robust systems.
  • Experience designing and building data versioning, artifact management, or dataset lineage systems (e.g., DVC, LakeFS, Weights & Biases, or custom solutions).
  • Hands-on experience with ML pipeline orchestration tools (e.g., Airflow, Prefect, Metaflow, or similar).
  • Experience with model serving and inference optimization - profiling latency, reducing memory footprint, or scaling serving infrastructure to meet real-time constraints.
  • Ability to read and refactor ML training code - you don't need to design model architectures, but you need to understand what training pipelines are doing well enough to make them reliable.
  • Proficient with Python, PyTorch.

Bonus qualifications
  • Familiarity with AWS infrastructure services.
  • Experience with containerized ML workflows and GPU-accelerated training environments.
  • Experience with model optimization techniques (e.g., quantization, TensorRT, ONNX Runtime, distillation).
  • Knowledge of infrastructure-as-code tools (e.g., AWS CDK, Terraform).
  • Experience building or operating ML systems that handle large unstructured datasets (imagery, 3D data, sensor data).

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