Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems
Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Macon, GA · On-site
Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Macon, GA · On-site
Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems
Sr. Solution Architect
Macon, GA · On-site
Enable IT development and infrastructure teams to make informed technology decisions through ... Guide the integration of AI/ML capabilities into analytics platforms , including predictive ...
Sr. Solution Architect
Macon, GA · On-site
Enable IT development and infrastructure teams to make informed technology decisions through ... Guide the integration of AI/ML capabilities into analytics platforms , including predictive ...
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Macon, GA · On-site
Familiarity with large-scale training and production ML systems. * Interest in building AI systems ... Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute ...
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Macon, GA · On-site
Familiarity with large-scale training and production ML systems. * Interest in building AI systems ... Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute ...
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Macon, GA · Remote
Familiarity with large-scale training and production ML systems. * Interest in building AI systems ... Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute ...
Applied Research Intern, Proactive Intelligence & Customer World Models (PhD / Graduate Co-op)
Macon, GA · Remote
Familiarity with large-scale training and production ML systems. * Interest in building AI systems ... Access to large-scale datasets, modern infrastructure, frontier models, and substantial compute ...
Ml Infrastructure information
See Perry, GA salary details
$41.9K - $53.1K
1% of jobs
$53.1K - $64.2K
1% of jobs
$64.2K - $75.3K
4% of jobs
$75.3K - $86.4K
9% of jobs
$86.4K - $97.5K
10% of jobs
$98.1K is the 25th percentile. Wages below this are outliers.
$97.5K - $108.6K
10% of jobs
The median wage is $113.1K / yr.
$108.6K - $119.7K
39% of jobs
$122.1K is the 75th percentile. Wages above this are outliers.
$119.7K - $130.8K
7% of jobs
$130.8K - $141.9K
10% of jobs
$141.9K - $153K
9% of jobs
$153K - $164.2K
1% of jobs
$41.9K
$114.6K
$164.2K
How much do ml infrastructure jobs pay per year?
As of Sep 5, 2026, the average yearly pay for ml infrastructure in Perry, GA is $114,608.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,000.00 and $127,200.00 per year, depending on experience, location, and employer.
What is ML infrastructure?
ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.
What are some common challenges faced by professionals working in ML infrastructure roles?
Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.
What are the key skills and qualifications needed to thrive as an ML infrastructure engineer, and why are they important?
To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.
What is the difference between Ml Infrastructure vs Data Engineer?
| Aspect | ML Infrastructure | Data Engineer |
|---|---|---|
| Required Credentials | Bachelor's in CS, Data Science, or related; knowledge of cloud platforms | Bachelor's in CS, Software Engineering, or related; experience with databases and ETL tools |
| Work Environment | Focus on deploying and maintaining ML systems, cloud environments, and infrastructure tools | Designing, building, and managing data pipelines and storage solutions |
| Industry Usage | Used in AI/ML teams to support model deployment and scalability | Used across data-driven organizations for data management and analytics |
ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.
What cities near Perry, GA are hiring for Ml Infrastructure jobs?
Cities near Perry, GA with the most Ml Infrastructure job openings:
Full-time
Re-posted 20 days ago
Job description
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Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch
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Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration
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Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues
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Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy
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Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams
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Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics
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Implement methods from recent ML papers quickly and turn them into production-grade systems