Machine Learning Engineer, Agentic AI- Worldwide Product Marketing

Apple, Inc.; Cupertino (CA); On-site; USD 185,000 - 278,000; Full time

Benefits offered by this job

Medical & dental

Retirement benefits

Stock programs

Education reimbursement

Relocation aid

Job summary

Apple, Inc. in Cupertino, California is seeking a Machine Learning Engineer to design and deploy AI/ML systems—from predictive models to agentic applications—that scale across business processes.

You will bring solutions from research to production-grade deployment with a growing focus on autonomous agents that reason, plan, use tools, and act for users and processes. You will work cross-functionally to identify high-value agentic use cases and translate them into reliable, safe, measurable

Qualifications

• 4+ years of building production ML or high-throughput applications.
• Proficiency in Python and distributed systems.
• Experience with Spark, SQL, Snowflake or similar big data technologies.
• Solid ML fundamentals including supervised/unsupervised learning, evaluation, and feature engineering.
• Experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers.
• Experience deploying large language models (LLMs) via APIs or self-hosted inference.
• Hands-on experience building agentic systems — tool calls, multi-step reasoning, memory, and RAG.
• Excellent cross-functional communication across engineering and data science teams.

Responsibilities

• Collaborate with MLEs to build scalable, production-ready ML solutions from concept to deployment.
• Design end-to-end AI/ML solutions with clear business impact, focusing on feasibility and performance.
• Benchmark, adapt, and integrate ML models into existing systems.
• Conduct rigorous model evaluation, testing, and iteration to improve quality and efficiency.
• Design, build, and deploy LLM-powered agents and multi-agent systems with human-in-the-loop oversight.
• Apply prompt and context engineering, outputs, and model routing to balance quality, latency, and cost.
• Ground agent decisions in quantitative predictions using classical ML tools.
• Deploy, monitor, and support agents and models in production with end-to-end tracing and observability.

Skills

Python

Distributed systems

ML fundamentals

LLM frameworks

Agentic systems

Tools

Spark

SQL

Snowflake

PyTorch

TensorFlow

scikit-learn

Hugging Face Transformers

LangGraph

LangChain

LlamaIndex

AutoGen

vLLM

TGI

Job description

Machine Learning Engineer, Agentic AI- Worldwide Product Marketing

Cupertino, California, United States Corporate Functions

As a Machine Learning Engineer, you will design and build AI/ML systems — from classical predictive models to LLM-powered agentic applications — that drive meaningful business outcomes at scale. You will take solutions from research and experimentation through to robust, production-grade deployment, with a growing focus on autonomous agents that reason, plan, use tools, and act on behalf of users and business processes. You will work cross-functionally to identify high-value agentic use cases and turn them into reliable, safe, and measurable products.

Description

The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.

Responsibilities

• Collaborate with other MLEs to build scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment
• Design end-to-end AI/ML solutions with clear business impact, with a strong focus on feasibility, scalability, and performance
• Benchmark, adapt, and integrate ML and foundation models into existing systems
• Conduct rigorous model evaluation, testing, and iteration to continuously improve model quality and efficiency
• Design, build, and deploy LLM-powered agents and multi-agent systems that plan, reason over multiple steps, and complete complex business workflows with human-in-the-loop oversight
• Apply prompt and context engineering, structured outputs, and model routing to balance quality, latency, and cost across agent workflows
• Integrate classical ML models (e.g., forecasting, classification, ranking) as tools within agent workflows to ground agent decisions in quantitative predictions
• Deploy, monitor, and support agents and models in production with end-to-end tracing and observability (per-step traces, token usage, failure modes), feeding insights back into iteration
• Partner with data scientists, engineers, and business stakeholders to identify high-value agentic use cases and translate business requirements into technical ML solutions
• Contribute to shared ML and agent infrastructure, reusable components, tooling, and best practices across teams

Minimum Qualifications

• 4+ years of related experience building high-throughput, scalable applications or machine learning models in a production environment
• Proficiency in Python with hands-on experience building distributed systems
• Experience building large-scale machine learning systems using big data technologies such as Spark, SQL, Snowflake, or similar platforms
• Solid understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering
• Experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers
• Experience building and deploying applications on large language models (e.g., Claude, GPT, Gemini, Llama, or other open-source models) via APIs or self-hosted inference (e.g., vLLM, TGI)
• Hands-on experience building agentic systems — tool/function calling, multi-step reasoning, memory, and RAG — with frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or similar
• Experience evaluating LLM and agent outputs, including quality assessment, hallucination detection, task-success metrics, and latency benchmarking in production
• Strong problem-solving skills with the ability to translate ambiguous business problems into well-defined ML and agentic solutions
• Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams

Preferred Qualifications

• Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

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Apple accepts applications to this posting on an ongoing basis.