This role pays more than
98%
of similar roles. Most pay
$177,900–$254,750
— the shaded band above.
At the midpoint, this role pays about
$335k
versus about
$216k
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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
Senior Engineering Manager, Conversational AI & Knowledge Intelligence leads the engineering organization responsible for conversational AI, world knowledge question answering, retrieval-augmented generation (RAG), and knowledge intelligence. This role involves managing 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. The manager defines the vision and architecture for platforms powering Siri, Spotlight, Safari, and Apple Intelligence. Key responsibilities include developing pipelines to curate and distill information into high-quality assets, optimizing LLM inference on device and server-side, and improving factuality and hallucination mitigation. The role requires expertise in large language models, NLP, distributed systems, vector databases, knowledge graphs, and reinforcement learning. The work focuses on creating accurate, grounded, and personalized responses by integrating proprietary knowledge graphs with trusted web content and foundation models.
What does a Engineering Manager earn in California?
Median $290250 from 64 postings across 19 companies.
Define the vision, architecture, and execution strategy for Conversational AI and Knowledge Intelligence platforms across the Apple ecosystem.
Lead end-to-end engineering and research for RAG, conversational reasoning, knowledge distillation, and LLM inference optimization.
Develop scalable pipelines to acquire, curate, and distill information from diverse sources into high-quality assets for large language models.
Partner with foundation model teams to improve retrieval quality, hallucination mitigation, and factual consistency of AI responses.
Build on-device and server-side software frameworks for low-latency, privacy-preserving, and cost-efficient LLM inference.
Mentor engineering managers and technical leaders while establishing long-term technical strategy and organizational direction.
Execute multi-year roadmaps to deliver high-quality products at Apple's scale across various product lines.
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 executing complex cross-functional programs involving multiple engineering organizations.
Expertise in modern LLM architectures, agentic AI, retrieval systems, semantic search, vector databases, embedding models, and knowledge graphs.
Experience in LLM post-training, reinforcement learning (RLHF, RLAIF), reward modeling, model alignment, and production deployment of large-scale AI systems.
Proven ability to build high-performing teams and translate emerging AI technologies into impactful customer experiences.