ATP Sense Engineering Manager

About Ford and ATP

Ford Motor Company has endured and thrived for more than 118 years by reimagining how things are built. Today, Ford is leading a once-in-a-generation transformation in manufacturing — scaling EVs, integrating humanoid robotics, additive manufacturing, AI vision, and digital platforms into one seamless operating system: the Ford Production System (FPS/FPS+) that operates within the FAST framework. Ford Motor Company is undergoing a historic industrial shift — from building great vehicles to building great systems that scale across them. The Advanced Industrial Technology & Platforms (AITP) organisation was created to operationalise that shift. Our job is not to deploy isolated tools; it is to build a system that learns from factory truth, transforms it into scalable platforms, and turns those platforms into enterprise business value. Advanced Industrial Technology and Platforms (ATP) is a new, centralised organisation designed to position Ford to lead industrial innovation. This team will accelerate our digital evolution and manufacturing excellence — advancing the modernisation of our Ford Manufacturing operations with cutting-edge technology. As part of Ford Manufacturing, ATP is designed to integrate directly into our manufacturing sites, partner across teams (GME, safety, etc.) and drive innovation from initial idea to full implementation.

ATP is led by Managing Director Yung Fung, reporting to Ford Chief Manufacturing Officer Bryce Currie. This structure includes four vertical business units — focused on specific technology stacks — and four horizontals — shared services designed to ensure scale, deployment, and operational consistency.

As part of the AITP organisation, the Sight Vertical is responsible for building an end-to-end business around AI/ML anomaly detection products, operating across two product families: image-based detection (Vision) and parametric data-based detection (Sense). The Sense family — comprising Sense.Machine (MiniTerms), Sense.Tooling (AI ToolSense), Sense.MILO, Sense.Stamping and Sense.Energy — is Ford's scalable platform for Condition Based Maintenance (CBM), process quality assurance, and zero-downtime, zero-defect manufacturing.

How Sense Works

Sense operates through value-focused, cross-functional product teams. Product Managers lead the product problem, desired outcome and priority. Engineers and product designers work with them to discover and deliver effective solutions. The Sense Engineering Manager leads people, capacity, technical-system and engineering-health accountability.

The Opportunity

We are looking for a Sense Engineering Manager to build and lead the engineering organisation behind Ford's parametric anomaly detection portfolio.

This is a people-leadership role. Your product is the engineering team and the technical system it owns. You will be accountable for the capability, capacity, technical integrity and operating health of Engineering across five Sense product streams — Machine, Tooling, Stamping, Energy and MILO — as they progress from proven plant-level value to global scale.

Sense products already run at meaningful scale: over 36,000 active MiniTerms at Valencia, multiple neural networks in production for AI ToolSense, and MILO deployed in production test stands globally. Your mandate is to make that scale sustainable, repeatable and safe — reducing bespoke plant engineering, industrialising deployment, and building an engineering team that is not dependent on any single individual.

The Position

Reporting to the Head of Product — Sense, you will directly manage the Sense Engineering Team, as well as managing relationships with capacity-increasing purchase service staff. You will provide the Engineering professional home for engineers embedded in product teams, and assign engineers to work streams with input from the Head of Product and Product Managers.

You will work closely with Sense Product Managers, product designers, and the Reach & Launch team, and partner with ATP Core, cybersecurity, data and infrastructure teams across Ford. Requires availability to travel internationally to Ford manufacturing sites globally, where required.

Success in Role

• A capable, healthy and appropriately sized Sense Engineering Team is in place, with clear ownership, active development plans and strong retention.
• Engineering capacity, demand and resource gaps are visibly and honestly managed — no unfunded work is absorbed silently.
• Architecture principles, decision records and technical standards exist and are used across streams.
• Sense products achieve their committed TRL/MRL/IRL gates on reproducible, defensible technical evidence.
• Engineering effort per plant deployment falls measurably; deployment cycle time compresses.
• A sustainable L2/L3 support model operates with named service owners, current runbooks and a declining rate of recurring incidents.
• Single-person dependencies are demonstrably reduced across all critical Sense services.
• Sense engineering practices are recognised as a reference standard within the Sight Vertical and ATP.

Minimum Requirements

• Bachelor's degree in Computer Science, Software Engineering, Electrical/Electronic Engineering or a related technical field.
• Demonstrated people-management experience leading software or data engineering teams — with direct accountability for performance, development, recruitment and team culture.
• Significant hands-on software engineering background (typically 8+ years) with the credibility to govern architecture and challenge technical decisions without owning every one.
• Experience delivering and operating production software — including CI/CD, release management, observability, incident management and a defined support model.
• Experience of cloud-native architecture (GCP preferred) and of integrating with operational technology: PLCs, industrial protocols (OPC-UA, MQTT, Modbus) and edge compute.
• Experience delivering AI/ML or data-intensive products into production, and working effectively with data scientists and data engineers.
• Proven capacity and prioritisation discipline — able to make trade-offs transparent and escalate constructively rather than over-commit the team.
• Strong global collaboration and stakeholder skills across multiple regions and time zones.
• Fluent professional English.

Preferred Requirements

• Master's or PhD in a software, data or systems engineering discipline.
• Experience managing engineers embedded in a product-led operating model (stream-aligned teams, empowered Product Managers).
• Experience of manufacturing or industrial software at scale — MES, Maximo, digital twins, condition-based maintenance.
• Experience building an engineering function from a small base, including hiring plans and capability roadmaps.
• Experience in a matrixed corporate environment influencing without direct line authority.
• Additional European language (Spanish or German particularly valuable given team locations).

Engineering People Leadership

• Lead, develop and grow the Sense Engineering Team — setting clear role, performance and development expectations for every engineer.
• Run regular one-to-ones, coaching, feedback and performance reviews, and build individual development and career plans.
• Recruit, onboard and retain engineering talent for approved positions, and identify capability and succession gaps before they become delivery risks.
• Create opportunities for engineers to develop technical leadership, delegating meaningful technical authority rather than making every decision personally.
• Build an inclusive culture of quality, learning, collaboration and production ownership, and actively monitor workload and burnout risk.

Stream-Aligned Engineering Organisation

• Establish stable workstream-aligned engineering assignments where product priorities and capacity allow, and ensure each stream has sufficient technical ownership.
• Assign engineers to product streams with input from the Head of Product and Product Managers, making movement between streams explicit.
• Prevent engineers from being spread across too many concurrent priorities, balancing stable product context against the need for specialist support.

Engineering Strategy and Architecture Governance

• Own the Sense Engineering strategy and technical roadmap, and establish architectural principles and proportionate technical standards.
• Ensure systems are scalable, reliable, secure, observable and maintainable across cloud, edge and plant environments.
• Make technical debt, obsolescence and architectural risk visible, and maintain lightweight architecture and decision-review practices.
• Partner with enterprise architecture, CoreDS, AIR, FPS+, cybersecurity and infrastructure teams, ensuring product and feature convergence choices are evidence-led rather than automatic.

Capacity and the Delivery System

• Make capacity and demand transparent across Build, Launch, Run and platform work, jointly with the Head of Product.
• Provide feasibility, effort, dependency and risk evidence before commitments are accepted, and maintain capacity guardrails and WIP limits.
• Escalate when portfolio demand cannot fit available capacity, and escalate valuable-but-unresourced work.

Engineering Excellence and Sustainable Run

• Establish proportionate quality standards and Definitions of Done, with effective testing, code review, CI/CD and release control.
• Design and operate the Sense L2/L3 support model with clear L1/L2/L3 responsibilities, named technical owners, current runbooks and escalation paths.
• Establish incident management and learning-review practices, and prioritise elimination of recurring incidents.
• Implement and use engineering metrics to improve systems.

Deployment Industrialisation and Technical MRL Evidence

• Own technical deployment and commissioning readiness, reducing bespoke plant engineering through automation, reusable patterns and standards.
• Ensure infrastructure, identity, access, integration and security requirements are addressed ahead of each launch, partnering with plant, Launch and Reach teams.
• Own the integrity and completeness of Engineering evidence for MRL progression, ensuring evidence reflects genuine readiness rather than administrative completion.

Discovery and Knowledge Distribution

• Embed engineers in product discovery from the beginning, so feasibility, data, integration, security and operational risks are examined early.
• Encourage engineers to engage directly with plant users at the GEMBA, and avoid substantial build commitments before critical technical assumptions are tested.
• Hold senior engineers accountable for mentoring and knowledge transfer, build communities of practice, and prevent specialists from becoming permanent bottlenecks.