This role pays more than
92%
of similar roles. Most pay
$177,900–$254,750
— the shaded band above.
At the midpoint, this role pays about
$318k
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.
As the Senior Engineering Manager, Conversational AI & Knowledge Intelligence, you will lead a multi-disciplinary team of machine learning engineers, software engineers, data engineers, and applied scientists within the AI, Search & Knowledge Platforms organization. You will define the vision and architecture for platforms powering Siri, Spotlight, Safari, and Apple Intelligence. Your daily work involves overseeing large-scale Retrieval-Augmented Generation (RAG), world knowledge understanding, conversational reasoning, and LLM inference optimization. You will build scalable knowledge pipelines to curate and distill information from diverse sources into high-quality assets. The role requires expertise in large language models, NLP, distributed systems, vector databases, and knowledge graphs. You will solve complex problems regarding hallucination mitigation, factuality, and low-latency retrieval across on-device and server-side frameworks to provide accurate, grounded, and personalized answers for users across the ecosystem.
What does a Engineering Manager earn in Washington?
Median $267050 from 35 postings across 10 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 Retrieval-Augmented Generation (RAG), conversational reasoning, and LLM inference optimization.
Develop scalable knowledge pipelines to acquire, curate, and distill information from diverse sources into high-quality assets.
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 multiple 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, knowledge graphs, and multi-stage ranking.
Experience in LLM post-training, reinforcement learning (RLHF, RLAIF), reward modeling, policy optimization, model alignment, and hallucination reduction.
Proven ability to build large-scale knowledge generation, distillation, content understanding, and enrichment pipelines.