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Machine Learning Engineer I Jobs in Georgia (NOW HIRING)

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Senior Machine Learning Engineer

Atlanta, GA · On-site

$100K - $138K/yr

As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and ...

Senior Machine Learning Engineer

Atlanta, GA · On-site

$100K - $138K/yr

As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and ...

Senior Machine Learning Engineer

Atlanta, GA · On-site

$100K - $138K/yr

As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and ...

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

... Machine Learning driven features with Python (including NumPy, SciPy, Pandas, TensorFlow, Pytorch) Other Qualifications: * Strong programming skills in Python with proficiency in relevant libraries ...

Showing results 41-60

Machine Learning Engineer I information

See Georgia salary details

$26.6K

$108.7K

$163.4K

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

As of Aug 18, 2026, the average yearly pay for machine learning engineer i in Georgia is $108,730.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,700.00 and $130,900.00 per year, depending on experience, location, and employer.

What is a Machine Learning Engineer I?

A Machine Learning Engineer I is an entry-level professional who designs, builds, and deploys machine learning models within software applications. They work closely with data scientists and software developers to implement algorithms that allow computers to learn from data and make predictions or decisions. Typical responsibilities include cleaning and preparing data, training models, evaluating performance, and optimizing algorithms for scalability and efficiency. This role often requires knowledge of programming languages like Python, frameworks such as TensorFlow or PyTorch, and a solid understanding of statistics and machine learning principles.

What projects can a Machine Learning Engineer I expect to work on during their first year?

As a Machine Learning Engineer I, you can expect to work on projects such as data preprocessing, building and testing basic machine learning models, and implementing existing algorithms under the guidance of senior team members. You'll often collaborate with data scientists, software engineers, and product managers to translate business requirements into technical solutions. Early projects may also involve model evaluation, feature engineering, and helping to deploy models into production environments. This hands-on experience helps build a strong foundation for tackling more complex problems as you advance in your career.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer I?

To thrive as a Machine Learning Engineer I, you need a solid foundation in programming (especially Python), mathematics, and machine learning concepts, typically supported by a bachelor’s degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, version control systems (e.g., Git), and cloud platforms is often expected. Strong problem-solving abilities, teamwork, and effective communication help you collaborate with stakeholders and translate business needs into technical solutions. These skills are crucial for building robust models, integrating them into production environments, and driving impactful results in data-driven projects.

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

AspectMachine Learning Engineer IData Scientist
Required CredentialsBachelor's in CS, Math, or related field; some roles may prefer certifications in ML or data analysisBachelor's or higher in Statistics, Data Science, or related field; often requires knowledge of programming and statistics
Work EnvironmentDevelops, tests, and deploys ML models; collaborates with data engineers and software developersAnalyzes data, builds models, and provides insights; works closely with business teams and analysts
Employer & Industry UsageTech companies, startups, and industries implementing AI solutionsFinance, healthcare, marketing, and research sectors relying on data-driven decisions

Machine Learning Engineer I focuses on developing and deploying ML models, while Data Scientists analyze data to generate insights. Both roles require programming skills and a background in math or statistics, but their daily tasks and objectives differ slightly.

Infographic showing various Machine Learning Engineer I job openings in Georgia as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 21% Part Time, 6% Contract, and 3% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $108,730 per year, or $52.3 per hour.

Machine Learning Engineer 3 (AI Engineer)

4pconsultinginc

Atlanta, GA • On-site

Contractor

Re-posted 3 days ago


Job description

Machine Learning Engineer 3 (AI Engineer)

Location: Atlanta, GA

Client- Southern Company Gas

Contract- 1 Year
 


Job Summary

We are seeking a highly skilled Machine Learning Engineer (Level 3) with 5–10 years of experience to design, develop, and deploy advanced AI models and systems. This role requires expertise in machine learning, data analysis, and model deployment to optimize business operations and drive innovation within the utilities and energy sector.

The successful candidate will collaborate with cross-functional teams—including data scientists, engineers, and business stakeholders—to integrate AI solutions into real-world applications that support operational efficiency, customer service, and sustainability initiatives.


Key Responsibilities
  • AI Model Development: Design and implement machine learning models and algorithms to address utility-specific challenges such as grid optimization, asset reliability, predictive maintenance, and customer analytics.

  • Data Analysis: Analyze large, complex datasets from SCADA, AMI, and IoT systems to extract actionable insights.

  • Model Training & Evaluation: Train, test, and validate AI models to ensure accuracy, scalability, and compliance with industry reliability standards.

  • Deployment & Integration: Deploy AI solutions into production systems and integrate with enterprise platforms (e.g., Azure, Maximo, EMS/DMS systems).

  • Innovation: Stay current with the latest advancements in AI/ML and recommend solutions that can enhance grid resilience, safety, and efficiency.

  • Collaboration: Partner with engineering, IT, and business units to define requirements and deliver business-aligned AI solutions.

  • Performance Monitoring: Continuously monitor AI models and refine as needed to maintain performance and compliance.

  • Documentation & Knowledge Sharing: Create clear documentation of models, workflows, and processes for reuse and compliance.


Qualifications

Education:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.

Experience:

  • 5–10 years of experience in AI, ML, or data science roles, with proven success in AI model development and deployment.

  • Industry experience in utilities, energy, or large-scale infrastructure data is preferred.

Technical Skills:

  • Proficiency in Python, R, or Java.

  • Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn.

  • Strong grasp of data structures, algorithms, and applied statistics.

  • Familiarity with cloud platforms such as Azure ML and Azure Databricks (preferred), AWS or Google Cloud (a plus).

  • Experience with big data tools (e.g., Spark, Hadoop) is desirable.

  • Exposure to natural language processing (NLP) or computer vision a plus.

Soft Skills:

  • Strong analytical and problem-solving abilities.

  • Excellent communication skills for cross-functional collaboration.

  • Ability to work independently and manage multiple projects simultaneously.

  • Experience working in agile or iterative development environments.


Preferred Qualifications
  • Lighting up AI/ML use cases in the utility/energy sector (e.g., outage prediction, DERMS optimization, vegetation management analytics).

  • Certifications in AI/ML, data science, or cloud platforms (Azure, AWS, GCP).

  • Experience with MLOps pipelines and CI/CD integration for model deployment.