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Apple Machine Learning Engineer Jobs in Atlanta, GA

Iterable

Senior Machine Learning Engineer (Nova)

Atlanta, GA

$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 ...

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

Redan, 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

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

Smyrna, 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 ...

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Showing results 1-20

All JobsApple Machine Learning Engineer JobsApple Machine Learning Engineer Jobs in Atlanta, GA

Apple Machine Learning Engineer information

See Atlanta, GA salary details

$30.3K

$123.8K

$186.1K

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

As of Jun 14, 2026, the average yearly pay for apple machine learning engineer in Atlanta, GA is $123,832.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,600.00 and $149,100.00 per year, depending on experience, location, and employer.

What collaboration opportunities can an Apple Machine Learning Engineer expect when working on cross-functional projects?

As an Apple Machine Learning Engineer, you will frequently collaborate with cross-functional teams including software engineers, product managers, and user experience designers. This collaboration is essential for integrating machine learning solutions seamlessly into Apple’s products and services. You can expect to participate in regular meetings to align on project goals, share technical insights, and troubleshoot challenges together. Such teamwork not only enhances product quality but also offers valuable opportunities for professional growth and skill development within Apple’s innovative environment.

What does an Apple Machine Learning Engineer do?

An Apple Machine Learning Engineer designs, develops, and implements machine learning models and algorithms that power Apple's products and services. They work with large datasets, collaborate with software and hardware teams, and contribute to features such as Siri, image recognition, and personalized recommendations. Their role involves researching new techniques, optimizing models for performance and efficiency, and ensuring privacy and security standards are maintained.

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

AspectApple Machine Learning EngineerApple Data Scientist
Required CredentialsBachelor's or Master's in CS, ML, or related fields; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDeveloping ML models, algorithms, deploying on Apple devicesAnalyzing data, building insights, supporting product decisions
Employer & Industry UsageTech industry, Apple-specific projects, hardware/software integrationTech industry, product analytics, user behavior insights

Apple Machine Learning Engineers focus on developing and deploying ML models within Apple's ecosystem, while Apple Data Scientists analyze data to inform product decisions. Both roles require strong technical skills, but ML Engineers are more involved in model creation and deployment, whereas Data Scientists focus on data analysis and insights.

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

To thrive as an Apple Machine Learning Engineer, you need a strong background in computer science, mathematics, and statistics, typically with experience in machine learning algorithms and a relevant degree. Expertise in programming languages such as Python or Swift, familiarity with frameworks like TensorFlow or PyTorch, and knowledge of Apple's Core ML are commonly required. Strong problem-solving abilities, creativity, and effective communication help you collaborate across teams and translate complex ideas. These skills ensure innovative, scalable, and user-centric machine learning solutions that align with Apple's high standards.
What are the most commonly searched types of Apple Machine Learning Engineer jobs in Atlanta, GA? The most popular types of Apple Machine Learning Engineer jobs in Atlanta, GA are:
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Apple Machine Learning Engineer jobs near you

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 10 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.