Forward Deployed Engineer - Advanced Manufacturing

In this position...

While we leverage industry-standard platforms for core ERP, CRM, MES, quality, and warehouse capabilities, our highest-value problems live at the manufacturing edge - where machines, sensors, operators, controls systems, production data, and cloud software meet. These are not generic applications for generic business functions. They are custom-built solutions for real production constraints: increasing throughput, improving uptime, reducing scrap, strengthening traceability, accelerating launches, and giving manufacturing leaders better real-time visibility into performance.

We are seeking a Forward Deployed Engineer to sit at the intersection of software engineering, manufacturing operations, industrial data, cybersecurity, and business impact. This is a hands-on individual contributor role for an engineer who can embed with plant teams, understand how automated manufacturing systems actually run, and then design, build, integrate, and deploy the software that helps those systems perform better in a highly secured manufacturing technology environment. You'll work across the full stack: application development, cloud infrastructure on GCP, manufacturing data pipelines, IIoT and edge architectures, MQTT-based messaging, OPC-UA connectivity, integrations with MES and automation systems, secure network and data flows, and analytics that turn equipment and production data into operational action.

This role is built for engineers who are comfortable on the factory floor and in the codebase. You will not be handed a fully specified spec; you will work with manufacturing, controls, maintenance, quality, process engineering, product, and operations teams to define the problem, validate the data, understand the constraints, and then build and deploy the answer.

We are building Ford Energy's manufacturing engineering capability to be software-defined and AI-native from day one: using connected assets, industrial protocols, real-time data, analytics, automation, and applied AI/ML to improve how facilities launch, run, learn, and scale.

This is an onsite role reporting to the Software Engineering Manager.

Ford Energy is a newly formed, wholly-owned subsidiary of Ford Motor Company dedicated to accelerating U.S. energy independence. Leveraging Ford's century of manufacturing excellence and world-class battery energy storage systems (BESS) technology, Ford Energy designs, manufactures, and services grid-scale and commercial DC battery energy storage systems (BESS). Ford Energy is uniquely positioned to capture the growing demand for reliable, US-built energy storage systems. We are not just building batteries; we are building the infrastructure for the next generation of the American grid. At Ford Energy, you have the backing of an industrial manufacturing powerhouse with the agility of a dedicated energy startup offering industry leading technology. We offer a competitive compensation package including performance-based bonuses, Ford vehicle discounts, and the opportunity to shape the energy strategy of one of the world's most iconic brands. Learn more at https://fordenergy.com.

What you'll do...

• Build the Manufacturing Edge: Design, develop, and deploy software systems that connect automated equipment, sensors, PLC-adjacent data, MES workflows, quality systems, warehouse systems, and cloud platforms. Build the custom edge capabilities that make highly automated facilities observable, reliable, and continuously improving.
• Own Industrial Data Flows: Work with and design IIoT architectures, MQTT messaging primarily, OPC-UA connectivity, edge gateways, time-series data, event-driven systems, and manufacturing data models to move trustworthy data from equipment and processes into applications, analytics, and decision-making workflows.
• Embed on the Floor and Solve: Work directly with manufacturing operations, controls, maintenance, quality, process engineering, product, and business stakeholders to understand production problems firsthand, then translate those needs into shipped software that works in a real facility environment.
• Deliver Manufacturing Analytics: Build analytics and operational intelligence capabilities for throughput, uptime, downtime, OEE, quality, scrap, traceability, cycle time, bottlenecks, energy usage, and launch readiness. Turn raw industrial data into insights, alerts, dashboards, models, and workflows that drive measurable operational improvement.
• Own Delivery End to End: Wear multiple hats across application engineering, cloud infrastructure, edge deployment, integrations, automation, observability, and production support. Success means a deployed capability improving manufacturing outcomes, not a handoff between specialized roles.
• Build Fast Without Breaking the Plant: Move quickly from ambiguity to production-ready systems while respecting manufacturing realities: safety, reliability, change control, cybersecurity, secure access, network segmentation, data protection, uptime, latency, data quality, maintainability, and the operational consequences of deploying software near physical processes in a highly secured environment.
• Engineer for Secure Operations: Design and operate solutions for highly secured manufacturing environments where identity, access control, secrets management, network boundaries, auditability, vulnerability management, and secure software delivery are core requirements. Build systems that can be trusted in environments where uptime, safety, intellectual property, and production continuity matter.
• Apply AI Where It Wins: Use generative AI, applied ML, anomaly detection, predictive analytics, intelligent automation, and AI-assisted development where they create measurable value for manufacturing operations. Every AI use case should have a clear operational purpose, cost justification, and path to safe adoption.
• Uphold the Engineering Standard: Contribute high-quality design and code, participate in architecture and code reviews, define durable integration patterns for industrial systems, and help establish the software, data, security, and deployment standards that allow highly automated facilities to scale.

What you'll do...

• Build the Manufacturing Edge: Design, develop, and deploy software systems that connect automated equipment, sensors, PLC-adjacent data, MES workflows, quality systems, warehouse systems, and cloud platforms. Build the custom edge capabilities that make highly automated facilities observable, reliable, and continuously improving.
• Own Industrial Data Flows: Work with and design IIoT architectures, MQTT messaging primarily, OPC-UA connectivity, edge gateways, time-series data, event-driven systems, and manufacturing data models to move trustworthy data from equipment and processes into applications, analytics, and decision-making workflows.
• Embed on the Floor and Solve: Work directly with manufacturing operations, controls, maintenance, quality, process engineering, product, and business stakeholders to understand production problems firsthand, then translate those needs into shipped software that works in a real facility environment.
• Deliver Manufacturing Analytics: Build analytics and operational intelligence capabilities for throughput, uptime, downtime, OEE, quality, scrap, traceability, cycle time, bottlenecks, energy usage, and launch readiness. Turn raw industrial data into insights, alerts, dashboards, models, and workflows that drive measurable operational improvement.
• Own Delivery End to End: Wear multiple hats across application engineering, cloud infrastructure, edge deployment, integrations, automation, observability, and production support. Success means a deployed capability improving manufacturing outcomes, not a handoff between specialized roles.
• Build Fast Without Breaking the Plant: Move quickly from ambiguity to production-ready systems while respecting manufacturing realities: safety, reliability, change control, cybersecurity, secure access, network segmentation, data protection, uptime, latency, data quality, maintainability, and the operational consequences of deploying software near physical processes in a highly secured environment.
• Engineer for Secure Operations: Design and operate solutions for highly secured manufacturing environments where identity, access control, secrets management, network boundaries, auditability, vulnerability management, and secure software delivery are core requirements. Build systems that can be trusted in environments where uptime, safety, intellectual property, and production continuity matter.
• Apply AI Where It Wins: Use generative AI, applied ML, anomaly detection, predictive analytics, intelligent automation, and AI-assisted development where they create measurable value for manufacturing operations. Every AI use case should have a clear operational purpose, cost justification, and path to safe adoption.
• Uphold the Engineering Standard: Contribute high-quality design and code, participate in architecture and code reviews, define durable integration patterns for industrial systems, and help establish the software, data, security, and deployment standards that allow highly automated facilities to scale.