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Machine Learning Infrastructure Jobs (NOW HIRING)

Jobzhr

$350 - $500/hr

  • PTO

Build and scale the infrastructure and data pipelines behind Safeguards machine learning research * Own the training, evaluation, and scoring workflows researchers use, with a focus on cutting the ...

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Machine Learning Infrastructure information

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How much do machine learning infrastructure jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for machine learning infrastructure in the United States is $28.01, according to ZipRecruiter salary data. Most workers in this role earn between $21.88 and $30.29 per hour, depending on experience, location, and employer.

What are the typical challenges faced by professionals working in machine learning infrastructure roles?

Professionals in Machine Learning Infrastructure often encounter challenges related to scaling systems to handle large datasets, ensuring model reproducibility, and maintaining efficient workflows for both development and deployment. Collaborating closely with data scientists, software engineers, and DevOps teams is crucial to address issues like version control, resource allocation, and performance optimization. Staying updated on evolving tools and cloud platforms is also essential, as the landscape changes rapidly and impacts system design and integration.

What are the key skills and qualifications needed to thrive in machine learning infrastructure, and why are they important?

To excel in Machine Learning Infrastructure, you need a solid background in computer science, software engineering, and distributed systems, often supported by experience in deploying and scaling machine learning models. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and ML workflow systems (e.g., TensorFlow Extended, MLflow) is crucial. Strong problem-solving skills, collaboration, and the ability to communicate technical concepts effectively help you stand out in this field. These skills ensure scalable, reliable, and efficient deployment of ML solutions, enabling organizations to leverage machine learning at production scale.

What is the difference between Machine Learning Infrastructure vs Data Engineer?

AspectMachine Learning InfrastructureData Engineer
Required CredentialsBachelor's in CS, experience with ML toolsBachelor's in CS, experience with data pipelines
Work EnvironmentFocus on ML systems, cloud platformsData pipelines, database management
Employer & Industry UsageTech companies, AI startupsAny industry with data needs, tech firms
Search & Comparison IntentUnderstanding ML system setupBuilding data pipelines

Machine Learning Infrastructure specialists focus on deploying and maintaining systems that support machine learning models, often working with cloud platforms and ML tools. Data Engineers build and manage data pipelines and databases, supporting data collection and processing. While both roles require technical skills and overlap in data handling, Machine Learning Infrastructure is more centered on ML system deployment, whereas Data Engineers focus on data architecture and pipelines.

What does a machine learning infrastructure engineer do?

A machine learning infrastructure engineer designs, builds, and maintains the systems and tools that support machine learning workflows, including data pipelines, model deployment, and scalable computing resources. They often work with cloud platforms, containerization, and automation tools to ensure efficient and reliable model training and deployment environments.
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What job categories do people searching Machine Learning Infrastructure jobs look for?

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Machine Learning Infrastructure jobs near you
Infographic showing various Machine Learning Infrastructure 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 $58,269 per year, or $28 per hour.

Senior Machine Learning Infrastructure Engineer, Embedding Platform

Reddit

Remote

$111K - $151K/yr

Full-time

Medical, Retirement, PTO

Posted 6 days ago

Job description

The LS Embedding Machine Learning Platform team is at the forefront of building highly expressive, machine learning models that power Reddit's recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit's ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.

About the Role

As a Senior Machine Learning Infrastructure Engineer, you will work across both model development and ML platform to build large-scale learning systems that improve recommendation and personalization on Reddit. At the senior level, you will own major technical components end to end: designing models, implementing training and evaluation pipelines, and driving production deployment in close partnership with ML platform, product, and cross-functional ML teams.

Responsibilities
  • Design, train, and improve large-scale machine learning platforms for recommendation or personalization systems.
  • Own and deliver major ML systems components end to end, from problem framing through production rollout.
  • Build and optimize end-to-end ML pipelines spanning data preparation, feature generation, training, evaluation, and deployment.
  • Improve distributed training, model efficiency, and online inference performance.
  • Apply modern modeling approaches including sequence modeling and related foundation-model techniques to Reddit use cases.
  • Develop reliable serving and monitoring patterns for low-latency, high-throughput production ML systems.
  • Work with cross-functional partners across product, relevance, ads, and core ML teams to deliver measurable improvements in user experience and business impact.
  • Drive rigorous offline and online evaluation, including experimentation, model diagnostics, and feedback-loop improvement.
  • Contribute to engineering quality through strong code, design reviews, documentation, and operational excellence.
Qualifications
  • 5+ years of experience in machine learning engineering, with a strong focus on large-scale ML infrastructure and recommendation or personalization systems.
  • Expertise in modern deep learning architectures, including sequence models and foundational models.
  • Experience building or scaling ML platform for large datasets and high-traffic production environments.
  • Demonstrated ability to independently scope and execute ambiguous technical work, while owning high-quality implementation details.
  • Solid understanding of distributed training and inference concepts, such as data parallelism, model parallelism, pipeline parallelism, or related optimization techniques.
  • Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar.
  • Strong software engineering fundamentals, including system design, debugging, testing, and performance optimization.
  • Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.
  • Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders.

Benefits:

  • Comprehensive Healthcare Benefits and Income Replacement Programs
  • 401k with Employer Match
  • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
  • Family Planning Support
  • Gender-Affirming Care
  • Mental Health & Coaching Benefits
  • Flexible Vacation & Paid Volunteer Time Off
  • Generous Paid Parental Leave 

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