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Machine Learning Engineer - LLMs, Agent Systems, and Simulation Tooling, Siri Core Modeling

Job in Cupertino, Santa Clara County, California, 95014, USA
Listing for: Apple
Full Time position
Listed on 2026-11-05
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 150000 USD Yearly USD 150000.00 YEAR
Job Description & How to Apply Below

Apple’s Siri organization is hiring a Senior Machine Learning Engineer to advance scalable LLM reasoning systems and agent-based simulation and evaluation tooling for agentic voice experiences. The role spans infrastructure for training and experimentation, alongside integration of agent behaviors across client and backend environments.

What You’ll Do
  • Define how next-generation intelligent agents reason, plan, and interact with people through natural voice and multimodal experiences
  • Develop foundations for scalable LLM reasoning systems that support human-AI interaction
  • Build core reasoning and simulation capabilities for a future agentic voice platform
  • Advance approaches for how LLMs plan, adapt, and evaluate actions in realistic environments
  • Contribute to reliable and trustworthy AI agents through robust infrastructure and tooling
  • Design and run experiments in simulated environments and build scalable evaluation pipelines
  • Integrate agent behaviors across client-side and backend systems
  • Collaborate with ML scientists, applied researchers, and product engineers to transition early research into deployable systems
  • Support development of a platform enabling developers and end-users to build rich, voice-driven AI experiences
Key Responsibilities
  • Design and implement scalable simulation and evaluation frameworks for LLM-based agents
  • Translate novel reasoning approaches from ML researchers into production-grade systems
  • Develop infrastructure for training and experimentation with agentic behaviors
  • Integrate agent reasoning capabilities into both client-side and backend environments
  • Improve iteration speed by creating tooling for experimentation, prototyping, and deployment
Required Qualifications
  • Experience deploying LLM models in research or production contexts
  • Knowledge of adaptive feedback loops, reinforcement learning, or interactive agent design
  • Familiarity with client-backend integration for AI-driven applications
  • MS or PhD in Computer Science, Machine Learning, or a related field
  • Bachelor’s degree in Computer Science, Machine Learning, or related quantitative field, with 3+ years of relevant industry experience
  • Strong skills in Python (preferred) and at least one other programming language
  • Proven ML engineering experience, including system design, training pipelines, and deployment workflows
  • Deep understanding of agent-based simulation, agentic RAG systems, and LLM evaluation methodologies
  • Ability to balance long-term platform vision with pragmatic short-term delivery in fast-paced environments
Technologies
  • Python
  • Reinforcement learning
  • Agentic RAG systems
  • LLM evaluation methodologies
Role Details
  • Location: Cupertino, CA (onsite)
  • Experience Level: 3+ years
  • Education: Bachelor’s degree in Computer Science, Machine Learning, or related quantitative field
  • Salary: USD 150, per year
Benefits
  • Comprehensive medical and dental coverage
  • Retirement benefits
  • Discounted products and free services
  • Reimbursement for certain educational expenses, including tuition
  • Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
  • Eligibility for discretionary restricted stock unit awards
  • Can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
  • Additionally, the role might be eligible for discretionary bonuses or commission payments as well as relocation
  • Note:

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