Senior Engineering Manager, Conversational AI & Knowledge Intelligence

Apple Inc

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $267,800–$401,700 / yr
Posted: 13 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $216k

This role $335k

$151k most similar roles pay here $429k

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.

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At a glance

TL;DR · Senior Engineering Manager, Conversational AI & Knowledge Intelligence

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Senior Engineering Manager, Conversational AI & Knowledge Intelligence will lead a multi-disciplinary team of machine learning engineers, software engineers, data engineers, and applied scientists within the AI, Search & Knowledge Platforms organization. The role involves defining the vision and architecture for conversational AI, world knowledge question answering, retrieval-augmented generation (RAG), and knowledge intelligence to power products like Siri and Apple Intelligence. Responsibilities include building scalable knowledge systems, LLM-powered reasoning capabilities, and infrastructure for information retrieval and distillation. Key technical areas involve large language models, NLP, distributed systems, vector databases, knowledge graphs, and RLHF. The position focuses on solving complex problems in hallucination mitigation, factuality improvement, and inference optimization across on-device and server-side frameworks to provide accurate, grounded answers from structured and unstructured data sources while ensuring low-latency performance and privacy-preserving execution.

What does a Engineering Manager earn in California?

Median $290250 from 64 postings across 19 companies.

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Skills

LLM RAG NLP Knowledge Graphs Vector Databases Distributed Systems Core ML RLHF RLAIF Semantic Search Information Retrieval Machine Learning Inference Optimization Model Alignment Knowledge Distillation

What you'll do

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.
Manage and mentor a high-performing team of engineering managers and technical leaders to execute multi-year roadmaps.
Translate emerging AI technologies into seamless products across Siri, Spotlight, Safari, and Apple Intelligence.

What we're looking for

MS or Ph.D. in Computer Science, AI, Machine Learning, Information Retrieval, NLP, 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 information retrieval platforms.
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 across multiple engineering organizations.
Expertise in LLM post-training, 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.
Strong leadership skills with the ability to influence senior executives and foster a culture of technical excellence.