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Senior Machine Learning Engineer Jobs in Atlanta, GA

Tihinsurance

Machine Learning Engineer

Alpharetta, GA · On-site

... the machine learning function at a market-leading insurance company. As one of the first data ... Leverage continuous engineering practices to deliver business value regarding effectiveness of the ...

AEG

Staff Machine Learning Engineer

Atlanta, GA · On-site

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet ...

Waystar

Senior ML Engineer

Atlanta, GA · On-site

$100K - $138K/yr

They are seeking a Senior Machine Learning Engineer to develop robust AI systems utilizing Language Models and agentic architectures, focusing on the entire ML pipeline from data extraction to ...

Cox Automotive

Machine Learning Lead Engineer

Vinnings, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Pine Lake, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Decatur, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Pine Lake, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Chamblee, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

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Showing results 21-40

All JobsMachine Learning Engineer JobsSenior Machine Learning Engineer JobsSenior Machine Learning Engineer Jobs in Atlanta, GA

Senior Machine Learning Engineer information

See Atlanta, GA salary details

$57.2K

$121.7K

$176.5K

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

As of Sep 4, 2026, the average yearly pay for senior machine learning engineer in Atlanta, GA is $121,700.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,500.00 and $138,000.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What are the key skills and qualifications needed to thrive as a senior machine learning engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

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

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Atlanta, GA?

The most popular types of Machine Learning Engineer jobs in Atlanta, GA are:

What are popular job titles related to Senior Machine Learning Engineer jobs in Atlanta, GA?

For Senior Machine Learning Engineer jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Engineer jobs in Atlanta, GA look for?

The top searched job categories for Senior Machine Learning Engineer jobs in Atlanta, GA are:

What cities near Atlanta, GA are hiring for Senior Machine Learning Engineer jobs?

Cities near Atlanta, GA with the most Senior Machine Learning Engineer job openings:

Senior Machine Learning Engineer jobs near you
Infographic showing various Senior Machine Learning Engineer job openings in Atlanta, GA as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $121,704 per year, or $58.5 per hour.

Machine Learning Engineer III 4P/791

4P Consulting Inc.

Atlanta, GA • On-site

Contractor

Re-posted 5 days ago

Job description

Position:         Machine Learning Engineer III – AI/ML Product Engineering

Location:        Atlanta, GA

Duration:        5 Months
Client:            Southern Company Services

Southern Company Services is seeking an experienced Machine Learning Engineer III to develop scalable, reusable, and production-grade AI products for deployment across multiple operating companies.

This role will focus on Retrieval-Augmented Generation, multi-agent systems, natural language processing, model deployment, and cloud-based AI solutions. The ideal candidate will have strong software engineering skills, hands-on AI/ML experience, and expertise with Azure or Google Cloud Platform.

 

Key Responsibilities

·       Design and build modular, reusable AI components and services.

·       Develop scalable RAG solutions using structured and unstructured data.

·       Engineer multi-agent systems for task coordination, workflow automation, and decision support.

·       Build transcription and NLP pipelines for customer-interaction analysis.

·       Develop and fine-tune models using PyTorch, Hugging Face Transformers, LangChain, or similar frameworks.

·       Package and deploy models using Azure Machine Learning, Google Cloud Platform, or Databricks.

·       Integrate Databricks for data ingestion, feature engineering, experimentation, and model development.

·       Develop reusable libraries, APIs, templates, and engineering patterns.

·       Partner with MLOps, DevOps, data engineering, architecture, and product teams.

·       Implement monitoring for model performance, data drift, system usage, and operational reliability.

·       Ensure AI solutions meet enterprise security, privacy, compliance, scalability, and observability requirements.

·       Provide technical guidance to teams adopting shared AI products and components.

 

Required Qualifications

·       Strong experience developing and deploying production-grade AI and machine learning solutions.

·       Hands-on experience with RAG architectures, LLM applications, multi-agent systems, and NLP.

·       Experience with Azure AI services, Google Cloud Platform AI services, or Azure Machine Learning.

·       Proficiency with Python and frameworks such as PyTorch, Transformers, or LangChain.

·       Experience deploying scalable models and AI services in cloud environments.

·       Knowledge of APIs, software engineering practices, model monitoring, and MLOps.

·       Experience working with structured and unstructured datasets.

·       Strong communication, collaboration, analytical, and problem-solving skills.

 

Preferred Qualifications

·       Experience with Databricks, vector databases, embeddings, and semantic search.

·       Experience building reusable enterprise AI platforms or shared AI services.

·       Knowledge of model evaluation, data drift, observability, and responsible AI.

·       Familiarity with CI/CD, containers, Kubernetes, and cloud-native deployment.

·       Utility, energy, or regulated-industry experience is preferred.