Forward Deployed Engineer – Advanced Manufacturing, Ford Energy

Ford•Dearborn, MI

28d•$115,000 - $192,900•Hybrid

About The Position

Ford Energy is seeking a hands-on individual contributor to build and deploy custom software for manufacturing operations, focusing on the intersection of software engineering, manufacturing systems, industrial data, cybersecurity, and business impact. The role works across application development, GCP cloud infrastructure, manufacturing data pipelines, IIoT and edge architectures, MQTT messaging, OPC-UA connectivity, MES and automation integrations, secure data flows, and analytics. The engineer will embed with plant teams to understand production constraints, validate data, and deliver software that improves throughput, uptime, quality, traceability, launch readiness, and operational visibility in highly secured manufacturing environments. The role supports Ford Energy's goal of building software-defined, AI-native manufacturing capabilities using connected assets, industrial protocols, real-time data, analytics, automation, and applied AI/ML. It is described as hybrid, with a requirement to be onsite four or more days per week, and reports to the Software Engineering Manager. The position is initially employed by Ford and is expected to transition to Ford's wholly owned Battery Energy Storage Systems subsidiary within one year.

Requirements

At least 5 years of experience in software engineering, manufacturing systems engineering, industrial software engineering, solutions engineering, or a similar hands-on engineering role.
Strong background building production software, integrating systems, and working across application, cloud, edge, and data layers.
Demonstrated experience with manufacturing environments, automation systems, plant-floor data, MES integrations, industrial equipment, quality systems, maintenance workflows, or production operations.
Hands-on familiarity with IIoT architectures, MQTT, OPC-UA, edge gateways, industrial protocols, event-driven architectures, time-series data, sensor data ingestion, and secure connectivity between plant-floor systems and cloud platforms.
Ability to build or support manufacturing analytics for measures such as OEE, throughput, downtime, cycle time, bottlenecks, quality, scrap, traceability, energy consumption, and asset performance.
Experience with modern software and cloud technologies, containerized services, infrastructure-as-code, CI/CD, observability, and secure production operations.
Track record of taking ambiguous, high-stakes operational problems from initial discussion to working production software.
Comfort working onsite in changing manufacturing environments, troubleshooting real systems, and balancing delivery speed with safety, reliability, cybersecurity, maintainability, and uptime.
Excellent communication and relationship-building skills across software engineering, controls, operations, quality, maintenance, product, and business stakeholders.
Ability to work autonomously, take accountability for shipped systems, and operate with significant latitude.

Nice To Haves

Familiarity with highly automated facilities, controls-adjacent systems, and launch or ramp environments.
Experience with Go, Python, TypeScript, API design, OpenShift, GCP, or comparable cloud platforms.
Experience working in highly secured environments with identity and access management, secrets handling, vulnerability remediation, audit logging, network controls, and secure deployment practices.
Experience with data platforms, streaming architectures, and AI/ML-enabled automation.
Experience turning noisy operational data into trusted metrics, dashboards, alerts, and decision-support tools.

Responsibilities

Design, develop, and deploy software connecting automated equipment, sensors, PLC-adjacent data, MES workflows, quality systems, warehouse systems, and cloud platforms.
Design and operate IIoT architectures, MQTT messaging, OPC-UA connectivity, edge gateways, time-series data, event-driven systems, and manufacturing data models.
Work directly with manufacturing operations, controls, maintenance, quality, process engineering, product, and business stakeholders to identify production problems and deliver working software.
Build analytics and operational intelligence for throughput, uptime, downtime, OEE, quality, scrap, traceability, cycle time, bottlenecks, energy usage, and launch readiness.
Own delivery across application engineering, cloud infrastructure, edge deployment, integrations, automation, observability, and production support.
Develop production-ready systems while accounting for safety, reliability, change control, cybersecurity, secure access, network segmentation, data protection, uptime, latency, data quality, and maintainability.
Design and operate solutions for highly secured manufacturing environments, including identity and access control, secrets management, network boundaries, auditability, vulnerability management, and secure software delivery.
Apply generative AI, machine learning, anomaly detection, predictive analytics, intelligent automation, and AI-assisted development when they provide measurable manufacturing value and can be adopted safely.
Contribute high-quality designs and code, participate in architecture and code reviews, establish durable industrial integration patterns, and help define software, data, security, and deployment standards.