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Machine Learning Infrastructure Engineer information

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$46.5K

$127.1K

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How much do machine learning infrastructure engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for machine learning 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 a machine learning infrastructure engineer?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

What are the key skills and qualifications needed to thrive as a machine learning infrastructure engineer?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What are some common challenges faced by machine learning infrastructure engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

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Machine Learning Infrastructure Engineer jobs near you
Infographic showing various Machine Learning Infrastructure Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.

Machine Learning Infrastructure Engineer

Convectivecapital

Redwood City, CA • On-site

$140 - $240/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 17 days ago

Job description

Machine Learning Infrastructure Engineer

WindBorne Systems is supercharging weather forecasts with a unique proprietary data source: a global constellation of next‑generation smart weather balloons targeting the most critical atmospheric data. We design, manufacture, and operate our own balloons, using the data they collect to generate otherwise unattainable weather intelligence.

Our mission is to eliminate weather uncertainty, and in the process help humanity adapt to climate change, be that predicting hurricanes or speeding the adoption of renewables. We are building a future in which the planet is instrumented by thousands of our microballoons, eliminating gaps in our understanding of the planet and giving people and businesses the information they need to make critical decisions. The founding team of Stanford engineers was named Forbes 2019 30 under 30 and is backed by top‑tier investors, including Khosla Ventures and Footwork VC.

WindBorne builds AI weather models that run 24/7, producing global forecasts every 20 minutes. Our research team is small and moves fast — but too much of their time goes to operationalization and infra firefighting instead of model development. We need someone to fix that.

Responsibilities
  • Research to Operations pipelines — Our models serve real‑time forecasts to customers with strict latency requirements. You'd own uptime end‑to‑end: build health monitoring, improve logging, diagnose failures across nodes.
  • Inference scaling & compute strategy — We have an on‑prem cluster but also use cloud providers, especially for production deployments. You'd evaluate cost/performance tradeoffs across cloud options as we scale, and also help manage growing on‑prem resources for compute and storage.
  • Data pipelines & upstream reliability — Weather data comes from dozens of sources (satellites, government agencies, our own balloon observations) with varying schedules, incomplete documentation and sometimes failing or changing quality. You'd build pipelines for training and real‑time data that gracefully handle upstream delays, do QC checks on data, and add logging and alerting for a zoo of edge cases.
  • Training infrastructure — Make distributed training runs reliable. They die from silent OOMs, network faults, and storage issues. Build monitoring, auto‑recovery, and job scheduling so researchers can launch experiments with less need for babysitting them.
Requirements
  • Have experience running production ML systems — you’re not just good at fighting fires but also know how to build systems that don’t catch on fire.
  • Experience with large datasets.
  • Comfortable keeping up with fast‑paced model releases and building reliable custom deployments for them.
  • Experience with PyTorch, Docker, cursed memory management, compression and debugging network saturation.
  • Affinity for systems and structure — you can counterbalance a research team’s natural state of chaos with well‑organized infrastructure and clear processes.
Nice to haves
  • Experience with weather data, geospatial pipelines, or scientific computing.
  • Experience with very large datasets, on the petabyte scale.
  • Experience managing GPU clusters or job schedulers.
Benefits
  • 401(k)
  • Dental insurance
  • Health insurance
  • Vision insurance
  • Unlimited PTO
  • Stock Option Plan
  • Office food and beverages
Salary
  • $140k‑$240k. We are considering a range of backgrounds and experience levels for this position and adjust our offers accordingly to be competitive with market rates.
Location

1600 Bridge Pkwy, Redwood City, CA. In person required.

About WindBorne Systems

WindBorne Systems is based in Redwood Shores, California. Our technology is invented, designed, and manufactured in the USA.

© 2026 by WindBorne Systems

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