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Applied Machine Learning Intern Jobs in Georgia (NOW HIRING)

AppFolio

GA

$16.25 - $19.25/hr

You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise ... Define and drive the machine learning roadmap across Leasing products -- identifying where ML ...

Imerys

Industrial Engineer Intern

Sandersville, GA · On-site

$14 - $18.25/hr

... machine learning tool. Create work instructions for activities that have been a cause of quality ... The applied knowledge from college, with the support of the plant staff, will bring improvements ...

Imerys

Industrial Engineer Intern

Sandersville, GA · On-site

$14 - $18.25/hr

... machine learning tool. Create work instructions for activities that have been a cause of quality ... The applied knowledge from college, with the support of the plant staff, will bring improvements ...

Cliftonlarsonallen

Senior AI Engineer

Atlanta, GA · On-site

$100K - $138K/yr

This position blends applied machine learning, software engineering, cloud architecture, and end-to-end solution delivery. Success in this role requires a strong understanding that production AI ...

Kiongroup

Sr Applied AI Engineer-Finance

Atlanta, GA · On-site

$113K - $174K/yr

As the Applied AI / Machine Learning Engineer, you will play a handson role crafting, developing, deploying, and operating AI and ML solutions tailored to Finance use cases such as financial planning ...

Kiongroup

Sr Applied AI Engineer-Finance

Atlanta, GA · On-site

$113K - $174K/yr

As the Applied AI / Machine Learning Engineer, you will play a handson role crafting, developing, deploying, and operating AI and ML solutions tailored to Finance use cases such as financial planning ...

Saige

Marketing Intern

Atlanta, GA

$15 - $19.75/hr

... and machine learning to recommend meals, which we then cook out of our commercial kitchen and ... intern, you are also entitled to $25 in food credits for each week with at least 15 hours worked.

Saige

Marketing Intern

Atlanta, GA · On-site

$15 - $19.75/hr

... and machine learning to recommend meals, which we then cook out of our commercial kitchen and ... intern, you are also entitled to $25 in food credits for each week with at least 15 hours worked.

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Applied Machine Learning Intern information

What is the difference between Applied Machine Learning Intern vs Data Science Intern?

AspectApplied Machine Learning InternData Science Intern
Required SkillsMachine learning algorithms, programming (Python, R), data analysisStatistical analysis, data visualization, programming (Python, R)
Work EnvironmentDeveloping ML models, experimenting with algorithms, deploying modelsData cleaning, analysis, reporting insights
Industry UsageTech companies, AI startups, research labsBusiness analytics, market research, finance

Applied Machine Learning Interns focus on developing and deploying machine learning models, requiring knowledge of algorithms and programming. Data Science Interns typically handle data analysis, visualization, and reporting. While both roles involve data skills, applied ML interns work more on model implementation, whereas data science interns focus on insights and data interpretation.

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Applied Machine Learning Intern jobs near you
Machine Learning Engineer - LLMs and Agentic

Machine Learning Engineer - LLMs and Agentic

Oversight Systems Inc

Atlanta, GA • On-site

Full-time

Re-posted 25 days ago

Job description

About Oversight

Oversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.

Position Overview:

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.


Education, Experience and Skills

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.