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Machine Learning Specialist Jobs in Georgia (NOW HIRING)

Tech Stars Group LLC

Senior Platform Engineer

Atlanta, GA · Remote

$100.50K - $138K/yr

In this role, you will collaborate closely with both application developers and machine learning specialists to design and implement services using terraform and Google Cloud Platform (GCP)

4P Consulting Inc.

DATA SCIENTIST

Forest Park, GA

This role involves advanced analytics, machine learning, and strong problem-solving skills to ... Thanks and Regards Sr. Talent Acquisition Specialist Pankaj Mishra Pankaj.Mishra@4pconsultinginc ...

Novelis Global R&T

Modeling Scientist

Kennesaw, GA · On-site

Design, develop, and validate statistical and machine learning models for prediction, optimization ... Partner with wide range of internal specialists to develop solutions e.g., with product ...

Novelis

Modeling Scientist

Kennesaw, GA · On-site

Design, develop, and validate statistical and machine learning models for prediction, optimization ... Partner with wide range of internal specialists to develop solutions e.g., with product ...

Novelis Global R&T

Modeling Scientist

Kennesaw, GA · On-site

Design, develop, and validate statistical and machine learning models for prediction, optimization ... Partner with wide range of internal specialists to develop solutions e.g., with product ...

Relativity

Senior Applied Scientist

Atlanta, GA · On-site +1

Our team includes specialists with advanced postgraduate training and deep experience building and operating machine learning models at scale. We work closely with engineering, product, design, data ...

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Machine Learning Specialist information

See Georgia salary details

$17.3K

$45.5K

$81.9K

How much do machine learning specialist jobs pay per year?

As of May 28, 2026, the average yearly pay for machine learning specialist in Georgia is $45,533.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,900.00 and $51,100.00 per year, depending on experience, location, and employer.

What is a Machine Learning Specialist job?

A Machine Learning Specialist is a professional who designs, develops, and implements machine learning models and algorithms to solve complex problems. They work with large datasets, use statistical and computational techniques, and optimize models for accuracy and efficiency. Their role often involves data preprocessing, feature engineering, model selection, and deployment. They collaborate with data scientists, software engineers, and domain experts to integrate machine learning solutions into business applications.

What are the key skills and qualifications needed to thrive in the Machine Learning Specialist position, and why are they important?

To thrive as a Machine Learning Specialist, you need a strong background in mathematics, programming (Python, R), and data analysis, typically supported by a relevant degree in computer science or a related field. Experience with machine learning frameworks such as TensorFlow, PyTorch, and knowledge of cloud platforms like AWS or Azure is highly valued, and certifications in these can enhance your qualifications. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly make someone stand out in this field. These skills and qualifications are crucial for effectively developing, deploying, and explaining machine learning solutions to diverse stakeholders.

What are some common challenges a Machine Learning Specialist might face in this role?

Machine Learning Specialists often encounter challenges such as ensuring data quality, selecting appropriate algorithms, and scaling solutions for real-world application. Navigating the complexities of messy or incomplete datasets and tuning models for optimal performance can be demanding. Balancing innovation with practical deployment constraints, such as computational resources and integration with existing systems, is also common. To address these challenges, collaboration with data engineers, domain experts, and product teams is essential, and ongoing professional development helps specialists stay ahead in this rapidly evolving field.
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What job categories do people searching Machine Learning Specialist jobs in Georgia look for? The top searched job categories for Machine Learning Specialist jobs in Georgia are:
Machine Learning Specialist jobs near you
Infographic showing various Machine Learning Specialist job openings in Georgia as of May 2026, with employment types broken down into 2% As Needed, 60% Full Time, 36% Part Time, and 2% Contract. Highlights an 69% Physical, and 31% Remote job distribution, with an average salary of $45,533 per year, or $21.9 per hour.

Machine Learning Engineer

Five and Fly, LLC.

Atlanta, GA • On-site

Full-time

Posted 4 days ago

Job description

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.
 
The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.
Core ML/LLM Engineering
  • Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.
  • Implement RAG pipelines using vector databases.
  • Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.
  • Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
  • Integrate memory, evidence packs, and explainability modules into agentic pipelines.
  • Work hands-on with multiple LLM ecosystems:
    • OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).
    • Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).
    • Google Gemini (multimodal reasoning, advanced RAG integration).
    • Meta LLaMA (fine-tuned/custom models for domain-specific tasks).
Data & Infrastructure
  • Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
  • Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
  • Implement model monitoring, drift detection, and retraining pipelines.
  • Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
Research & Applied Innovation
  • Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
  • Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
  • Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
  • Translate research prototypes into production-ready components.
Collaboration & Delivery
  • Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
  • Participate in design reviews, architecture discussions, and model evaluations.
  • Document processes, experiments, and results effectively for knowledge sharing.
  • Mentor junior engineers and contribute to ML engineering best practices.
Required
  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
  • 3+ years of experience building and deploying ML systems.
  • Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
  • Demonstrated experience with at least two of the following ecosystems:
    1. OpenAI GPT models (chat, assistants, fine-tuning).
    2. Anthropic Claude (safety-first AI for reasoning and summarization).
    3. Google Gemini (multimodal reasoning, enterprise-scale APIs).
    4. Meta LLaMA (open-source, fine-tuned models).
  • Familiarity with vector databases, embeddings, and RAG pipelines.
  • Ability to work with structured and unstructured data at scale.
  • Knowledge of SQL and distributed data frameworks (Spark, Ray).
  • Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
Preferred Qualifications
  • Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
  • Knowledge of AI safety, guardrails, and explainability techniques.
  • Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
  • Experience with CI/CD for ML (MLOps), monitoring, and observability.
  • Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
  • Contributions to open-source AI/ML projects or publications in applied ML research.
US Army:
17D - Cyber Capability Developer
17C - Cyber Operations Specialist (Advanced Track)
35Q - Cryptologic Network Warfare Specialist
35N / 35P / 35S (Intel Analysts w/ coding exposure)
US AirForce:
17X - Cyberspace Warfare Operations
1B4X1 - Cyber Warfare Operations
9S100 - Scientific Applications Specialist
3D0X4 / 1D7X1 (Software / Data Ops variants)

US Navy:
CTN - Cryptologic Technician (Networks)
CTI / CTR (with analytics focus)
Information Warfare Officers (1810)
US Marine Corps:
1721 - Cyberspace Warfare Operator
26XX Intel (with data/automation focus)
US Space Force:
Cyber Operations (DCO/OCO) Guardians

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