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Machine Learning Engineer Quantization Jobs in Gray, GA

Southern Trust Insurance Company

Data Engineer

Macon, GA

$109K - $131K/yr

Exposure to machine learning/AI concepts. * Experience working with APIs and integrating external ... Data Engineering: Experience building and supporting data warehouses/lakes, especially on Snowflake ...

Novolex

Process Engineer

Macon, GA · On-site

Establishes set-up and processing parameters based on material, machine capabilities. * Develops ... Learning Management System that supports and enhances employee skills at all levels of the ...

Yancey Bros.co

Heavy Equipment Training Instructor

Macon, GA

$47K - $62K/yr

... learning more about our customers, offering equipment and support to keep up with their changing ... Lectures classes on safety, installation, programming, testing, maintenance and repair of machinery ...

Yancey Bros Co

Boot Camp Training Instructor

Macon, GA · On-site

$47K - $62K/yr

Schedule, demonstrate and manage the learning plan and day to day activities of the boot campers in ... Lectures classes on safety, installation, programming, testing, maintenance and repair of machinery ...

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Machine Learning Engineer Quantization information

See Gray, GA salary details

$29.2K

$119.4K

$179.4K

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

As of Jun 19, 2026, the average yearly pay for machine learning engineer quantization in Gray, GA is $119,397.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,100.00 and $143,700.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What cities near Gray, GA are hiring for Machine Learning Engineer Quantization jobs? Cities near Gray, GA with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Gray, GA as of June 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $119,397 per year, or $57.4 per hour.
Data Engineer

Data Engineer

Southern Trust Insurance Company

Macon, GA

$109K - $131K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 3 days ago

Job description

Data Engineer

Must be located in the Eastern or Central US Time Zone
Travel: Occasional, based on project needs
Reports To: Chief Information Officer

We are seeking a skilled, hands-on Data Engineer to join our team. In this role, you will build, test, and maintain data pipelines and data warehouse components that support our broader data platform. You will help enable data-driven decision-making by ensuring business teams have access to clean, reliable, and well-modeled data.

As a Data Engineer, you will transform raw data into actionable insights by developing scalable pipelines, creating robust data models, and supporting reporting and analytics tools in partnership with the broader team.

Key Responsibilities

  • Data Platform Development: Build and maintain data pipelines and data warehouse/lakehouse components (data warehouses, lakes, and marts) while following established data governance, security, and privacy controls.
  • Pipeline Engineering: Develop reliable data ingestion pipelines using scheduled and event-driven patterns. Optimize performance and ensure resilience.
  • Data Quality & Monitoring: Implement data quality frameworks, validation checks, and safeguards to minimize pipeline failures and data integrity issues.
  • Architecture & Optimization: Contribute to improving legacy ingestion methods, address technical debt, and support impact assessment for proposed changes in partnership with the team.
  • Reporting & Visualization: Build custom reports and dashboards using tools like Power BI, Snowflake, etc.
  • Collaboration & Agile Delivery: Work closely with product owners and cross-functional teams to understand business needs, clarify requirements, and deliver solutions using agile methodologies.
  • Automation & Efficiency: Identify inefficiencies, automate processes, and recommend improvements to optimize data flows.
  • Documentation & Standards: Document pipelines, datasets, and data models; follow team standards; and participate in code reviews to improve maintainability and consistency.
  • Production Support: Provide technical support for production incidents, ensuring system stability and continuous improvement.

Required Qualifications

Experience: 2–4 years in data engineering with experience in Snowflake, SQL, ELT/ETL, dimensional data modeling, data warehousing, and pipeline development.

Technical Skills:

  • Strong SQL skills with experience in query optimization and basic performance tuning.
  • Solid understanding of dimensional modeling and core data architecture principles.
  • Familiarity with Azure cloud services.
  • Experience with BI tools (Power BI & Snowflake).
  • Knowledge of Agile/Scrum practices.
  • Experience building dimensional (Kimball-style) data models.

Soft Skills:

  • Excellent communication (written and verbal).
  • Strong problem-solving and incident management capabilities.
  • High attention to detail and commitment to data accuracy.

Preferred Qualifications

  • Bachelor’s degree in Computer Science, Information Systems, or related field (or equivalent experience).
  • Proficiency in programming languages such as Python or Java.
  • Experience with orchestration tools and CI/CD pipelines (e.g., ADO YAML Pipelines or GitHub Actions).
  • Familiarity with cloud platforms (Azure) and modern data tools (Snowflake).
  • Exposure to machine learning/AI concepts.
  • Experience working with APIs and integrating external data sources.
  • Background in the insurance industry (including carrier experience) is a plus.

Technical Competencies

  • Data Engineering: Experience building and supporting data warehouses/lakes, especially on Snowflake and Azure.
  • Data Integration: Experience developing ELT/ETL pipelines using modern tools and scripting languages.
  • Data Modeling: Proficient in logical and physical data modeling using relational and dimensional approaches.
  • Performance Optimization: Experience tuning pipelines and database objects for optimal performance.
  • Version Control & CI/CD: Familiarity with ADO YAML Pipelines or GitHub Actions and automated deployment practices.
  • API Integration: Experience implementing and leveraging APIs for data exchange.

Compensation

  • Commensurate with experience
  • Performance-based incentives

Benefits Package

  • 401(k) company match up to 6% eligible upon hire
  • Medical, dental & vision, including company paid Life insurance and long-term disability
  • Health care flexible spending accounts
  • Paid time off
  • Parental & family leave; military leave & pay
  • Employee Referral Incentive
  • Career Development & Continuing Education Assistance

Physical Conditions/Requirements

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions. While performing the duties of this position, the employee is regularly required to talk or hear. The employee frequently is required to use hands or finger, handle, or feel objects, tools or controls. The employee is occasionally required to stand; walk; sit; reach with hands and arms; climb or balance; and stoop, kneel, crouch, or crawl. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by this position include close vision, distance vision, color vision, peripheral vision, and the ability to adjust focus. The noise level in the work environment is usually moderate.

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