Machine Learning Engineer, Agentic AI- Product Marketing

Position: Machine Learning Engineer, Agentic AI- Worldwide Product Marketing
Role Number:
Team:
Corporate Functions

Location:

Cupertino Weekly

Hours:

40

Posted:
Summary 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  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.

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, Tensor Flow, 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 Lang Graph, Lang Chain, Llama Index, Auto Gen, 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

Pay & Benefits 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…