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Senior Machine Learning Engineer Jobs in Georgia

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain ... senior guidance * Excellent understanding of model evaluation techniques, feature engineering ...

... building a modern, machine learning-driven search platform that powers intelligent product ... This opportunity sits within a newly formed Search Engineering team, working closely with senior ML ...

Staff Machine Learning Engineer

Atlanta, GA ยท On-site +1

$162K - $342K/yr

As a Staff Machine Learning Engineer , you will design, build, and deploy machine learning systems that power predictive analytics, personalization, automation, and intelligent platform behaviors.You ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Senior Machine Learning Engineer information

See Georgia salary details

$50.2K

$106.9K

$154.9K

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

As of Jun 23, 2026, the average yearly pay for senior machine learning engineer in Georgia is $106,862.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,200.00 and $121,200.00 per year, depending on experience, location, and employer.

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 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 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 Georgia? The most popular types of Machine Learning Engineer jobs in Georgia are:
What cities in Georgia are hiring for Senior Machine Learning Engineer jobs? Cities in Georgia with the most Senior Machine Learning Engineer job openings:
Next Insurance - Machine Learning Engineer

Next Insurance - Machine Learning Engineer

Beyond SOF

Atlanta, GA โ€ข On-site

Other

Posted 11 days ago


Job description

Machine Learning Engineer
Experience level:Mid-senior
Experience required:4 Years
Education level:Bachelor's degree
Job function:Information Technology
Industry:Financial Services
Total position:1
Relocation assistance:Limited assistance
Visa :Only US citizens and Greencard holders


Job Summary

We are looking for a driven Machine Learning Engineer to help us bring software development rigor to the ML life-cycle within the business insurance industry. As an experienced innovator partnering with the marketing and growth teams, this is a massive opportunity to drive high growth impact at a hyper-growth startup. Your job is to be a full stack ML engineer, supercharging all aspects of scaling Machine Learning at NEXT: application design and architecture, scalable deployment of inference solutions as APIs, data enrichment as a service, and model monitoring.

You will be joining an innovative division of NEXT based in Atlanta: Data Labs. The mandate of Data Labs is to build software and data solutions that meaningfully impact marketing, funnel, risk, and servicing/claims experiences.

What You'll Do:

  • Empower our team of data scientists to rapidly develop and deploy ML solutions.
  • Leverage software engineering best-practices to create and deploy data-intensive and machine learning inference products.
  • Understand the data and dig deep to extract actionable insights.
  • Think creatively and outside the box to answer desired experimental questions as well as exposing opportunities to create business value.
  • Work cross-functionally with marketing, engineering, product, senior management, and external partners.

What We Need:

  • 3+ years of hands-on experience in the complete software development life-cycle with demonstrated professional experience with machine learning
  • Strong command of Python and the standard web development frameworks (e.g. Django, Flask, FastAPI) & fluency with database technologies, SQL, and Python data packages
  • Demonstrated experience deploying models and applications to a cloud environment using tools like Docker and Kubernetes.
  • Demonstrated experience with software engineering best-practices, such as Test-Driven Development and continuous integration/deployment pipelining
  • Experience with ML-oriented data pipelining tools (dbt, Airflow, Prefect, DVC, etc.)

Unstoppable Qualities:

  • Experience in the insurance industry (strong fintech/lending experience will also be considered)
  • Experience with GitLab