below marketmost similar roles pay hereabove market
This role pays more than
97%
of similar roles. Most pay
$165,412–$254,750
— the blue band above.
At the midpoint, this role pays about
$335k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
The Senior Engineering Manager, Agentic AI & Intelligent Reasoning leads the engineering organization responsible for building next-generation AI systems within the AI, Search & Knowledge Platforms team. This leader manages machine learning engineers, software engineers, and applied scientists to develop agentic AI frameworks, reasoning systems, planning engines, memory architectures, and orchestration platforms. The role involves defining technical strategies for multi-step reasoning, multi-hop knowledge retrieval, and long-context reasoning to solve complex user intents. Key technologies and competencies include large language models, Retrieval-Augmented Generation, reinforcement learning, model alignment, and Private Cloud Compute. The work focuses on creating intelligent, context-aware experiences for Siri and Apple Intelligence by synthesizing world knowledge with personal context while maintaining strict privacy, reliability, and safety standards through robust evaluation frameworks and simulation environments.
What does a Engineering Manager earn in California?
Median $279400 from 84 postings across 24 companies.
Define the technical strategy and execution roadmap for Apple's Agentic AI answer and knowledge platform.
Lead end-to-end engineering to build AI agents capable of multi-step reasoning and decomposing complex user goals.
Develop agentic architectures that orchestrate LLMs, retrieval systems, planning modules, and memory systems.
Drive innovations in task decomposition, long-context reasoning, and tool use to improve answer relevance and factuality.
Partner with cross-functional teams to build scalable AI systems that integrate user context with trusted web information.
Lead the development of evaluation frameworks, safety guardrails, and reasoning benchmarks to measure agent quality.
Build and mentor a high-performing organization of engineering managers and technical leaders to execute multi-year roadmaps.
What we're looking for
MS or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related technical field.
8+ years of experience leading engineering organizations delivering large-scale AI, machine learning, or distributed systems in production.
Proven track record of delivering complex AI platforms from research through production at large scale.
Strong experience in large language models, conversational AI, intelligent agents, reasoning systems, or advanced machine learning platforms.
Demonstrated success leading cross-functional organizations and defining long-term technical strategy while executing complex engineering programs.
Deep expertise in agentic AI architectures, autonomous agents, reasoning systems, planning algorithms, tool use, memory architectures, multi-agent systems, and orchestration frameworks (preferred).
Strong engineering and R&D experience in LLMs, RAG, long-context reasoning, chain-of-thought optimization, RLHF, RLAIF, model alignment, and advanced context techniques (preferred).
Experience building systems for multi-intent understanding, multi-hop reasoning, deep research, evidence synthesis, complex workflow execution, and personalized AI experiences (preferred).
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Reinforcement Learning
RLHF
RLAIF
Natural Language Processing
Distributed Systems
Machine Learning
Model Alignment
Chain-of-Thought
Orchestration Frameworks
Evaluation Frameworks
Observability Systems
Continuous Learning Pipelines
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Reinforcement Learning
Natural Language Processing
Distributed Systems
Machine Learning
Chain-of-Thought
Model Alignment
RLHF
RLAIF
Orchestration Frameworks
Evaluation Frameworks
Observability Systems
Continuous Learning Pipelines
On-device Intelligence
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Natural Language Processing
Machine Learning
Reinforcement Learning
RLHF
RLAIF
Distributed Systems
Model Alignment
Chain-of-Thought
Orchestration Frameworks
Evaluation Frameworks
Observability Systems
Continuous Learning Pipelines
Private Cloud Compute
Conversational AI
LLM
Retrieval-Augmented Generation (RAG)
Natural Language Processing
Knowledge Graphs
Vector Databases
Reinforcement Learning
RLHF
RLAIF
Distributed Systems
Core ML
Information Retrieval
Semantic Search
Model Alignment
Knowledge Distillation
Embedding Models
Ranking Systems
Inference Optimization
Conversational AI
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs)
Knowledge Graphs
Vector Databases
Distributed Systems
Core ML
RLHF
RLAIF
Information Retrieval
Semantic Search
Model Alignment
Knowledge Distillation
Embedding Models
Ranking Systems
LLM Inference Optimization
Conversational AI
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs)
Knowledge Graphs
Vector Databases
Distributed Systems
Core ML
Information Retrieval
Semantic Search
Model Alignment
Knowledge Distillation
Embedding Models
Ranking Systems
LLM Inference Optimization
Private Cloud Compute