Senior Software Engineering Manager – Manufacturing Intelligence, Agentic Systems & Physical AI

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there is no telling what you could accomplish.

Apple’s Manufacturing and Product Operations organization is looking for a hands-on, technically accomplished Senior Software Engineering Manager to lead multiple software engineering teams building the next generation of intelligent manufacturing systems.

This organization develops the software platforms, agentic workflows, data infrastructure, and AI-powered applications that support complex manufacturing operations at global scale. The work spans manufacturing process optimization, production planning, quality inspection, equipment intelligence, supply and material workflows, and the emerging use of embodied and physical AI on the factory floor.

In this role, you will lead multiple teams responsible for building highly scalable, reliable, and secure systems that connect enterprise applications, manufacturing data, AI models, industrial equipment, and human decision-making. You will establish a cohesive technical strategy across these teams and work closely with manufacturing, operations, quality, test, automation, robotics, machine learning, and data engineering organizations to turn emerging technologies into dependable production capabilities.

Building intelligent systems for manufacturing introduces unique challenges: heterogeneous data, rapidly changing factory conditions, high reliability requirements, physical-world constraints, and decisions that can directly affect production. We are looking for an experienced organizational leader with strong software engineering depth, architectural judgment, manufacturing awareness, and a demonstrated ability to build, scale, and align high-performing engineering teams.

## Description

As a Senior Software Engineering Manager, you will lead multiple software and systems engineering teams responsible for designing, building, and operating intelligent platforms for <manufacturing.You> will define the technical strategy, organizational structure, and engineering roadmap across a portfolio of systems that combine distributed software, manufacturing data, machine learning, agentic AI, and physical automation. You will remain deeply engaged in architecture, design reviews, technical trade-offs, and the transition of early prototypes into secure, scalable, and operationally reliable production systems.

## Minimum qualifications

18+ years of software engineering experience, including substantial experience leading multiple engineering teams responsible for large-scale, business-critical systems

Demonstrated success managing managers, technical leads, senior individual contributors, or multiple engineering workstreams within a complex software organization

Experience defining organizational strategy, team charters, ownership models, technical roadmaps, and execution mechanisms across multiple teams

Strong hands-on technical foundation and the ability to provide credible guidance during architecture reviews, design discussions, and complex technical escalations

Experience building highly available distributed systems, microservices, data platforms, workflow engines, or enterprise integration platforms

Experience developing systems that interact with relational and non-relational databases, event streams, caching systems, object stores, APIs, and asynchronous processing frameworks

Strong understanding of system architecture, data structures, algorithms, concurrency, distributed computing, and production reliability

Experience designing platforms for AI, machine learning, data-intensive applications, or intelligent automation

Understanding of modern agentic-system concepts, including orchestration, tool use, retrieval, planning, state management, human-in-the-loop controls, evaluations, and observability

Ability to determine where probabilistic AI approaches are appropriate and where deterministic software, business rules, validation, or operator approval are required

Experience integrating software with complex enterprise systems, operational workflows, or heterogeneous data environments

Ability to translate ambiguous manufacturing and business problems into scalable software architectures, organizational plans, and executable engineering programs

Strong judgment in balancing investments across multiple teams while managing operational risk, technical debt, conflicting priorities, and aggressive schedules

Excellent written and verbal communication skills, including the ability to explain complex engineering and organizational issues in business and operational terms

Demonstrated ability to influence and collaborate across large organizations spanning software, machine learning, manufacturing engineering, operations, quality, automation, robotics and program management

Strong organizational leadership skills and a consistent track record of setting priorities, establishing accountability, resolving cross-team blockers, and delivering measurable outcomes

Proven ability to hire, mentor, retain, and develop engineers, technical leaders, and engineering managers

Bachelors or Masters degree in Computer Science, Software Engineering, Electrical Engineering, Robotics, or a related technical field, or equivalent practical experience

## Preferred qualifications

Experience leading software organizations supporting manufacturing, industrial automation, supply chain, quality, test engineering, or factory operations

Experience with manufacturing systems such as ERP, MES, PLM, QMS, WMS, equipment-control systems, or industrial data platforms

Experience building AI agents or workflow-automation systems that perform multi-step reasoning and interact with enterprise tools and APIs

Experience with computer vision systems for automated optical inspection, defect detection, process monitoring, or equipment intelligence

Exposure to robotics, industrial automation, edge computing, digital twins, simulation, or embodied and physical AI

Experience deploying AI or software capabilities on factory-floor or edge devices with constrained compute, latency, connectivity, security, or privacy requirements

Familiarity with technologies such as Java, Python, Spark, Kafka, Kubernetes, Docker, object storage, search platforms, vector databases, and modern cloud or hybrid infrastructure

Experience developing web applications and operational interfaces using frameworks such as React, Angular, or comparable technologies

Understanding of AI-system evaluation, model lifecycle management, security, responsible AI, and production governance