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Machine Learning Engineer Quantization Jobs in Lithonia, GA

Cox Automotive

Machine Learning Lead Engineer

Hapeville, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Iterable

Senior Machine Learning Engineer (Nova)

Atlanta, GA · On-site

$100K - $138K/yr

We are looking for a Senior Machine Learning Engineer to build the core Machine Learning foundations that power Nova's agentic experiences. This role focuses on applied Machine Learning in production ...

Cox Automotive

Machine Learning Lead Engineer

Riverdale, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Pine Lake, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Decatur, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Atlanta, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Morrow, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Hapeville, GA · On-site

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Lithonia, GA

Machine Learning Engineer Quantization information

See Lithonia, GA salary details

$28.8K

$117.6K

$176.7K

How much do machine learning engineer quantization jobs pay per year?

As of Jun 19, 2026, the average yearly pay for machine learning engineer quantization in Lithonia, GA is $117,561.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,700.00 and $141,500.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Lithonia, GA? For Machine Learning Engineer Quantization jobs in Lithonia, GA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Lithonia, GA look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Lithonia, GA are:
What cities near Lithonia, GA are hiring for Machine Learning Engineer Quantization jobs? Cities near Lithonia, GA with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 14 days ago

Job description

Position:           Machine Learning Engineer III – AI/ML Engineer

Location:          Atlanta, GA

Duration:          6 Months

Client:             Southern Company services

Job Summary

We are seeking an experienced Machine Learning Engineer III / AI-ML Engineer to support the development of reusable, scalable AI products that can be deployed across multiple operating companies and business units.

This role will focus on building production-grade AI solutions, including Retrieval-Augmented Generation (RAG), multi-agent orchestration, NLP pipelines, transcription solutions, model deployment, and reusable AI components for internal operational workflows.

The ideal candidate will have strong hands-on experience with GCP or Azure AI services, modern ML frameworks, strong software engineering skills, and a product-focused mindset.

Key Responsibilities

  • Design and build modular, reusable AI components that can scale across business units.
  • Lead development of scalable RAG-based solutions for document comparison and analysis.
  • Work with structured and unstructured data to support AI-driven business solutions.
  • Engineer multi-agent systems for intelligent task coordination and decision support.
  • Develop transcription and NLP pipelines for customer interaction analysis.
  • Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
  • Package models for deployment in GCP, Azure ML, and/or Databricks.
  • Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
  • Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
  • Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
  • Provide technical guidance to teams adopting reusable AI components.
  • Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
  • Implement monitoring for model performance, data drift, usage metrics, and production reliability.

Required Qualifications

  • Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
  • Strong experience building production-grade AI/ML solutions.
  • Hands-on experience with cloud-based AI services, preferably GCP or Azure.
  • Experience developing RAG-based applications using structured and unstructured data.
  • Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
  • Experience with frameworks and tools such as:
    • PyTorch
    • Transformers
    • LangChain
  • Experience deploying models in cloud or enterprise environments.
  • Strong programming and software engineering skills.
  • Ability to work with APIs, reusable components, and scalable architectures.
  • Experience collaborating with MLOps, DevOps, and data engineering teams.
  • Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
  • Experience designing multi-agent systems or AI orchestration workflows.
  • Experience developing transcription, NLP, or customer interaction analytics pipelines.
  • Experience with model monitoring, data drift detection, observability, and usage metrics.
  • Experience building shared AI libraries, reusable templates, or internal AI platforms.
  • Understanding of enterprise security, compliance, and governance requirements for AI products.
  • Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.