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Temporary Meta Machine Learning Jobs in Atlanta, GA

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

Machine Learning Engineer Employment Type: Full Time Location: Atlanta, GA Description We are ... Meta LLaMA (fine-tuned/custom models for domain-specific tasks). * Collaborate with Data ...

Tihinsurance

Machine Learning Engineer

Alpharetta, GA · On-site

Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st Shift (United States of America) Please review the following We are building the foundation of the machine learning ...

Meta

Associate General Counsel, Commercial

Atlanta, GA · On-site +1

$170K/yr

Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. Meta is committed to providing reasonable accommodations ...

PRGX Global, Inc

Artificial Intelligence Intern

Atlanta, GA · On-site

$14.50 - $19.25/hr

Job Title Artificial Intelligence Intern Employment Type Temporary Work Authorization Requirements ... Job Duties & Responsibilities • Apply machine learning algorithms and statistical models to large ...

PRGX Global, Inc

Artificial Intelligence Intern

Atlanta, GA · On-site

$14.25 - $19/hr

Job Title Artificial Intelligence Intern Employment Type Temporary Work Authorization Requirements ... Job Duties & Responsibilities · Apply machine learning algorithms and statistical models to large ...

PRGX Global, Inc

Artificial Intelligence Intern

Atlanta, GA · On-site

$14.50 - $19.25/hr

Job Title Artificial Intelligence Intern Employment Type Temporary Work Authorization Requirements ... Job Duties & Responsibilities • Apply machine learning algorithms and statistical models to large ...

PRGX Global, Inc

Artificial Intelligence Intern

Atlanta, GA · On-site

$14.25 - $19/hr

Job Title Artificial Intelligence Intern Employment Type Temporary Work Authorization Requirements ... Job Duties & Responsibilities · Apply machine learning algorithms and statistical models to large ...

LinkTag

Strategic Accout Manager - Analytics

Atlanta, GA · On-site

$100 - $125/hr

Our platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality ...

Monitise

AI Engineer

Alpharetta, GA · On-site

As an AI Engineer you will apply advanced machine learning and statistical techniques to detect ... Individuals with temporary visas including, but not limited to, F-1 (OPT, CPT, STEM), H-1B, H-2, or ...

Truist

Fraud Data Scientist II

Atlanta, GA · On-site

Regular or Temporary: Regular Language Fluency: English (Required) Work Shift: 1st shift (United ... machine learning modeling, etc.) to provide actionable insights that improve business outcomes and ...

U.S. Navy

Linguist

Mcdonough, GA

... and machine learning systems. • Perform temporary duty aboard Navy surface ships and submarines, based on assignment requirements. What to Expect CTIs have an intellectually demanding career ...

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How much do temporary meta machine learning jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for temporary meta machine learning in Atlanta, GA is $21.95, according to ZipRecruiter salary data. Most workers in this role earn between $18.94 and $24.52 per hour, depending on experience, location, and employer.

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

What are the key skills and qualifications needed to thrive as a temporary Meta machine learning engineer?

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What are the most commonly searched types of Meta Machine Learning jobs in Atlanta, GA?

The most popular types of Meta Machine Learning jobs in Atlanta, GA are:

What are popular job titles related to Temporary Meta Machine Learning jobs in Atlanta, GA?

For Temporary Meta Machine Learning jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Temporary Meta Machine Learning jobs in Atlanta, GA look for?

The top searched job categories for Temporary Meta Machine Learning jobs in Atlanta, GA are:

What cities near Atlanta, GA are hiring for Temporary Meta Machine Learning jobs?

Cities near Atlanta, GA with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you
Infographic showing various Temporary Meta Machine Learning job openings in Atlanta, GA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $45,648 per year, or $21.9 per hour.

Machine Learning Engineer

Atlanta, GA • On-site

Five and Fly
See more jobs at Five and Flyexternal

Full-time

Re-posted 19 days ago

Job description

Machine Learning Engineer
Department: Machine Learning Engineer
Employment Type: Full Time
Location: Atlanta, GA
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.
Key Responsibilities
  • 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).
  • 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.
  • 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.
  • 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.

Skills, Knowledge and Expertise
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.
  • 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.

Seeking following AFSC/MOSs
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 Operator26XX Intel (with data/automation focus)
US Space Force:Cyber Operations (DCO/OCO) Guardians

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