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Entry Level Machine Learning Engineer Jobs in Atlanta, GA

Bespoke Labs

Machine Learning Engineer

Kennesaw, GA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Kennesaw, GA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Atlanta, GA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Atlanta, GA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Crowe LLP

Machine Learning Engineer

Atlanta, GA · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

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Entry Level Machine Learning Engineer information

See Atlanta, GA salary details

$28.9K

$66.7K

$113.5K

How much do entry level machine learning engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for entry level machine learning engineer in Atlanta, GA is $66,702.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $75,500.00 per year, depending on experience, location, and employer.

What is an entry level machine learning engineer?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are some typical projects or tasks an entry level machine learning engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

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 Entry Level Machine Learning Engineer jobs in Atlanta, GA?

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

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

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

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

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

Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Atlanta, GA as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $66,702 per year, or $32.1 per hour.

Machine Learning Engineer III 4P/791

4P Consulting Inc.

Atlanta, GA • On-site

Contractor

Re-posted 4 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.