below marketmost similar roles pay hereabove market
This role pays more than
97%
of similar roles. Most pay
$176,000–$254,750
— the blue band above.
At the midpoint, this role pays about
$335k
versus about
$215k
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, Conversational AI & Knowledge Intelligence leads the engineering organization within the AI, Search & Knowledge Platforms team. This leader manages a team of machine learning engineers, software engineers, data engineers, and applied scientists to build scalable knowledge systems, LLM-powered reasoning capabilities, and retrieval infrastructure. Key responsibilities include defining the architecture for Conversational AI, managing Retrieval-Augmented Generation (RAG) pipelines, and developing on-device and server-side software frameworks. The role requires expertise in large language models, NLP, distributed systems, vector databases, and knowledge graphs. The work focuses on solving complex problems in world knowledge understanding, hallucination mitigation, and answer grounding to power conversational experiences across Siri, Spotlight, and Safari by synthesizing proprietary knowledge graphs with trusted web content.
What does a Engineering Manager earn in California?
Median $279400 from 84 postings across 24 companies.
Define the vision, architecture, and execution strategy for Apple's Conversational AI and Knowledge Intelligence platforms.
Lead engineering and research for Retrieval-Augmented Generation (RAG), conversational reasoning, and LLM inference optimization.
Build intelligent systems combining proprietary knowledge graphs, web content, and foundation models to deliver accurate answers.
Drive the development of scalable pipelines to acquire, curate, and distill information into high-quality knowledge assets.
Develop on-device and server-side software frameworks for low-latency, privacy-preserving retrieval and LLM orchestration.
Partner with foundation model teams to improve retrieval quality, hallucination mitigation, and factual consistency.
Build and mentor high-performing 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, Information Retrieval, Natural Language Processing, or a related technical field.
8+ years of experience leading large-scale engineering and machine learning organizations delivering AI-powered products in production.
Deep experience building conversational AI, search, question answering, knowledge systems, recommendation systems, or large-scale information retrieval platforms.
Strong technical expertise in large language models (LLMs), Retrieval-Augmented Generation (RAG), NLP, and distributed systems.
Demonstrated ability to define long-term technical strategy while successfully executing complex cross-functional programs involving multiple engineering organizations.
Deep expertise in modern LLM architectures, agentic AI, retrieval systems, semantic search, vector databases, embedding models, knowledge graphs, and multi-stage retrieval and ranking systems (preferred).
Strong engineering and R&D experience in LLM post-training, reinforcement learning, reward modeling, policy optimization, model alignment, grounding, and production deployment of large-scale AI systems (preferred).
Experience building large-scale knowledge generation, knowledge distillation, content understanding, and knowledge enrichment pipelines (preferred).
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
LLM Inference Optimization
Ranking Systems
Conversational AI
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs)
Knowledge Graphs
Vector Databases
Distributed Systems
Model Alignment
Knowledge Distillation
Semantic Search
Core ML
Information Retrieval
Ranking Systems
Embedding Models
LLM Inference Optimization
Knowledge Pipelines
Conversational AI
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs)
Knowledge Graphs
Vector Databases
Distributed Systems
Model Alignment
Knowledge Distillation
Semantic Search
Core ML
Information Retrieval
Ranking Systems
Embedding Models
LLM Inference Optimization
Knowledge Pipelines
Conversational AI
Retrieval-Augmented Generation (RAG)
Large Language Models (LLMs)
Knowledge Graphs
Vector Databases
Distributed Systems
Core ML
Information Retrieval
Semantic Search
Knowledge Distillation
Model Alignment
Embedding Models
LLM Inference Optimization
Knowledge Pipelines
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