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 systems 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 synthesize information from diverse sources into high-quality assets while managing on-device and server-side software frameworks for low-latency, privacy-preserving execution. 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 information retrieval to provide accurate, grounded, and personalized answers 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.
Manage and mentor a high-performing team of engineering managers and technical leaders to execute multi-year roadmaps.
Translate emerging AI technologies into seamless, high-quality products across Siri, Spotlight, Safari, and Apple Intelligence.
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.
Technical expertise in large language models (LLMs), Retrieval-Augmented Generation (RAG), NLP, and distributed systems.
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, model alignment, and production deployment of large-scale AI systems.
Experience building knowledge generation, distillation, content understanding, and enrichment pipelines to improve AI quality and freshness.