Senior Engineering Manager, Agentic AI & Intelligent Reasoning

Apple Inc

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Seattle, WA
Salary: $254,400–$381,600 / yr
Posted: 13 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $206k

This role $318k

$140k most similar roles pay here $407k

This role pays more than 92% of similar roles. Most pay $166,150–$246,150 — the shaded band above. At the midpoint, this role pays about $318k versus about $206k 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, Agentic AI & Intelligent Reasoning

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As the Senior Engineering Manager, Agentic AI & Intelligent Reasoning, you will lead a high-performing organization of machine learning engineers, software engineers, and applied scientists within the AI, Search & Knowledge Platforms team. You will define the technical strategy and execution roadmap for agentic AI frameworks, reasoning systems, planning engines, memory architectures, and orchestration platforms. Your daily work involves building systems capable of multi-hop reasoning, task decomposition, and evidence synthesis to solve complex user problems while maintaining strict privacy standards. You will leverage large language models, Retrieval-Augmented Generation, chain-of-thought optimization, reinforcement learning, and advanced context techniques. The role focuses on developing intelligent agents that orchestrate various components like retrieval systems and tool use to provide personalized, grounded answers across the ecosystem, ensuring high levels of factuality, safety, and reliability for sophisticated user interactions.

What does a Engineering Manager earn in Washington?

Median $267050 from 35 postings across 10 companies.

See salary data

Skills

Agentic AI Large Language Models Retrieval-Augmented Generation (RAG) Machine Learning Natural Language Processing Reasoning Systems RLHF RLAIF Distributed Systems Chain-of-Thought Multi-agent Systems Orchestration Frameworks Evaluation Frameworks Observability

What you'll do

Define the vision, technical strategy, and execution roadmap for Agentic AI and knowledge platforms.
Lead engineering and research to build agents capable of multi-hop reasoning and complex task decomposition.
Develop agentic architectures that orchestrate LLMs, retrieval systems, planning modules, and memory systems.
Drive innovations in long-context reasoning, tool use, and adaptive execution to improve answer factuality.
Partner with cross-functional teams to integrate user context and web information while maintaining strict privacy.
Build robust evaluation frameworks, safety guardrails, and observability systems to measure agent quality.
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, Natural Language Processing, or a related technical field.
8+ years of experience leading engineering organizations delivering large-scale AI, machine learning, or distributed systems in production.
Proven track record of delivering complex AI platforms from research through production at scale.
Strong experience in large language models, conversational AI, intelligent agents, reasoning systems, or advanced machine learning platforms.
Demonstrated success leading cross-functional organizations and defining long-term technical strategy while executing complex engineering programs.
Deep expertise in agentic AI architectures, autonomous agents, planning algorithms, tool use, memory architectures, and orchestration frameworks.
Experience in RAG, chain-of-thought optimization, reinforcement learning for LLMs (RLHF, RLAIF), and model alignment.
Ability to translate cutting-edge AI research into high-quality consumer products while balancing innovation with rapid execution.