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Director Google Machine Learning Engineer 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 ... Google Gemini (multimodal reasoning, advanced RAG integration). * Meta LLaMA (fine-tuned/custom ...

innovitusa

Machine Learning Engineer

Atlanta, GA · On-site

W2 Candidates Only We are seeking a Machine Learning Engineer to develop, deploy, and optimize machine learning models and AI solutions. The ideal candidate will have strong experience with Python ...

Equifax, Inc.

Machine Learning Engineer

Atlanta, GA · On-site

Equifax is excited to add a Machine Learning Engineer to our team. What you'll do * Design complex systems of systems for training and running machine learning models with industry best practice

Speria

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $160/hr

Job Summary We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team at Speria MTech. The ideal candidate will play a crucial role in designing, building, and ...

Omnissa

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$162K - $342K/yr

As a Staff Machine Learning Engineer , you will design, build, and deploy machine learning systems that power predictive analytics, personalization, automation, and intelligent platform behaviors.You ...

FanDuel

Senior Machine Learning Engineer

Atlanta, GA · On-site

$100K - $138K/yr

Experience working in a cloud environment such as AWS, Google Cloud Platform, Azure. * Experience ... direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and ...

Tihinsurance

Machine Learning Engineer

Alpharetta, GA · On-site

... the machine learning function at a market-leading insurance company. As one of the first data ... Leverage continuous engineering practices to deliver business value regarding effectiveness of the ...

Warnerbros

Senior Machine Learning Engineer

Atlanta, GA

$100K - $138K/yr

Senior Machine Learning Engineer Team: Data & Audience Platform (DAP) - ML Engineering What We Do ... Mosaic, FreeWheel, Google Ad Manager. Agentic AI: Cursor, GitHub Copilot, Amazon Q, Databricks ...

AEG

Staff Machine Learning Engineer

Atlanta, GA · On-site

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet ...

Patientco

Senior Machine Learning Engineer I

Atlanta, GA · On-site

$117K - $155K/yr

Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using ... Ph.D. preferred. * 5+ years of professional experience in machine learning engineering, with a ...

Waystar

Senior Machine Learning Engineer I

Atlanta, GA · On-site

$117K - $155K/yr

Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using ... Ph.D. preferred. * 5+ years of professional experience in machine learning engineering, with a ...

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Director Google Machine Learning Engineer information

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How much do director google machine learning engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for director google machine learning engineer in Atlanta, GA is $88,407.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,800.00 and $101,900.00 per year, depending on experience, location, and employer.

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Director Google Machine Learning Engineer jobs near you

Machine Learning Engineer

Five and Fly

Atlanta, GA • On-site

Full-time

Re-posted 12 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
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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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