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

Apple; Cupertino (CA); On-site; USD 150,000 - 278,000; Full time

Benefits offered by this job

Comprehensive medical and dental

Retirement benefits

Stock programs eligibility

Education expense reimbursement

Employee stock purchase plan discount

Relocation eligibility

Job summary

Apple is hiring a Senior Machine Learning Engineer in Cupertino to advance scalable LLM reasoning systems and agent-based simulation tooling for voice experiences.

You'll work on infrastructure for training and experimentation, and integrate agent behaviors across client and backend environments, collaborating with ML scientists and engineers to transition research into deployable systems.

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.

Responsibilities

• Design 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 client-side and backend environments.
• Improve iteration speed by creating tooling for experimentation, prototyping, and deployment.

Skills

LLM deployment

Python

Client-backend integration

Reinforcement learning

System design

Experimentation tooling

Education

MS/PhD in Computer Science, Machine Learning, or related field

Bachelor’s degree in CS, ML, or related quantitative field with 3+ years experience

Tools

Docker

ML pipelines

Job description

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 150,400 - 277,600 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