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Machine Learning Engineer Jobs in Georgia (NOW HIRING)

Machine Learning & Operations Engineer

Atlanta, GA ยท On-site +1

$66.90K - $90.50K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning.

Machine Learning Engineer II

Atlanta, GA ยท On-site

$93.80K - $128.40K/yr

As a Machine Learning Engineer II (AI Enablement), you will play a crucial role in designing, implementing, and operating the shared AI/ML platform capabilities that other engineers build on. You ...

Machine Learning Engineer II

Atlanta, GA ยท On-site

$93.80K - $128.40K/yr

As a Machine Learning Engineer II (AI Enablement), you will play a crucial role in designing, implementing, and operating the shared AI/ML platform capabilities that other engineers build on. You ...

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Machine Learning Engineer information

See Georgia salary details

$26.6K

$108.7K

$163.4K

How much do machine learning engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for machine learning engineer 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 Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate 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 Machine Learning Engineer jobs? Cities in Georgia with the most Machine Learning Engineer job openings:
What are popular job titles related to Machine Learning Engineer jobs in GA? For Machine Learning Engineer jobs in GA, the most frequently searched job titles are:
Machine Learning & Operations Engineer

Machine Learning & Operations Engineer

OptiTrack

Atlanta, GA โ€ข On-site, Remote

$66.90K - $90.50K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago


Job description

OptiTrack is a global leader in motion capture technology, delivering precision tracking solutions for animation, robotics, virtual production, biomechanics, and industrial applications.
About the Role
OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale an MLOps system and provide other support to teams working on projects involving machine learning. This role sits at the intersection of machine learning engineering and infrastructure, focusing on automation of data validation pipelines, orchestration of large-scale experiments, and deployment of high-performance algorithms.
This is a fully remote position, working cross-functionally with research and engineering teams.
What You'll Do
  • Design and maintain automated ML training pipelines.
  • Build infrastructure for large-scale distributed experimentation.
  • Develop CI/CD workflows tailored for machine learning systems.
  • Orchestrate data ingestion, preprocessing, validation, and model versioning.
  • Implement experiment tracking, hyperparameter tuning automation, and reproducibility systems.
  • Optimize GPU/compute utilization across cloud and on-prem environments.
  • Deploy, monitor, and maintain production ML models
  • Establish and enforce MLOps best practices including model registry, artifact management, and observability.
  • Improve system reliability, performance, and security.
  • Collaborate closely with ML researchers make new algorithms product ready.
  • More typical DevOps responsibilities for software development as required.

Requirements
Required Qualifications
  • 3+ years of experience in MLOps, ML infrastructure, Machine Learning, or related roles or relevant degree experience.
  • Experience with Python and ML frameworks (PyTorch, TensorFlow, or similar)
  • Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.)
  • Hands-on experience with containerization (Docker) and orchestration
  • Experience managing GPU workloads and distributed training systems
  • Experience with cloud platforms (AWS, GCP, or Azure)
  • Strong understanding of automation, infrastructure reliability, and data pipelines
  • Ability to work with both European and US developers.

Preferred Qualifications
  • Experience with motion capture or computer vision systems
  • Familiarity with experiment tracking tools (MLflow, Weights & Biases, etc.)
  • Background in distributed systems or high-performance computing
  • Experience with workflow orchestration tools (Airflow, Argo, Prefect, Kubeflow)
  • Infrastructure as Code experience (Terraform, Pulumi, CloudFormation)
  • Experience with model optimization, inference acceleration, or edge deployment
  • Experience building tracking algorithms for device localization using techniques like SLAM
  • Strong problem-solving skills and attention to reproducibility
  • Comfortable working in a remote, collaborative environment, with international team members
  • Clear communicator who can bridge research and production engineering
  • Experience with image rendering pipelines in CryEngine.
  • Passion for building scalable AI infrastructure

Why Join OptiTrack?
  • Work on cutting-edge motion tracking systems
  • Contribute to technology used across robotics, animation, virtual reality, biomechanics, and virtual production
  • Remote-first flexibility
  • Opportunity to shape the next-generation of motion capture technology

Benefits
All benefits start on first day of employment!
  • 75% employer-paid medical for employee. Family coverage also included.
  • 100% employer paid dental, and vision for employee and dependents
  • 100% employer paid long-term, short-term disability, and life insurance policy
  • 401k Match, if you're contributing 5% we match 4%. 100% vested immediately.
  • 10 paid holidays
  • Starting at 15 days paid PTO (inclusive of sick and vacation time) annually
  • Employee Assistance Program (EAP)
  • Flexible Spending Account (FSA)

EEOC Statement:
OptiTrack is an equal opportunity employer, we believe in fostering a culture of equality, diversity, and inclusivity. Our commitment to this goal is clearly expressed in our zero-tolerance policy for discrimination and harassment of any kind, including on the basis of race, color, sex, age, religion, sexual orientation, national origin, disability, genetic information, pregnancy, protected veteran status or any other characteristic protected by applicable federal, state, or local laws. Our hiring practices ensure that decisions are based solely on qualifications, merit, and current business needs, while extending to all aspects of our operations - from recruitment and promotion to layoff and recall, to leave of absence, compensation, benefits, and training. We are committed to remaining a drug free workplace