Forward Deployed Engineer Advanced Manufacturing - Ford Energy

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. 

You'll have...

• Technical Depth: 5+ years of experience as a software engineer, manufacturing systems engineer, industrial software engineer, solutions engineer, or similar hands-on engineering role. Strong background in building production software, integrating systems, and operating across application, cloud, edge, and data layers.
• Manufacturing and Industrial Systems Experience: Demonstrated experience working with manufacturing environments, automation systems, plant-floor data, MES integrations, industrial equipment, quality systems, maintenance workflows, or production operations. Familiarity with highly automated facilities, controls-adjacent systems, and launch/ramp environments is strongly preferred. 
• IIoT and Edge Connectivity: 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 is required. 
• Manufacturing Analytics: Ability to build or support analytics for OEE, throughput, downtime, cycle time, bottleneck detection, quality, scrap, traceability, energy consumption, and asset performance. Experience turning noisy operational data into trusted metrics, dashboards, alerts, and decision-support tools is highly valued. 
• Modern Stack Experience: Experience with Go, Python, TypeScript, API design, OpenShift, GCP, or comparable cloud platforms, containerized services, infrastructure-as-code, CI/CD, observability, and secure production operations. Experience working in highly secured environments with identity and access management, secrets handling, vulnerability remediation, audit logging, network controls, and secure deployment practices is strongly preferred. Experience with data platforms, streaming architectures, and AI/ML-enabled automation is a plus. 
• Bias for Deployed Outcomes: A track record of taking ambiguous, high-stakes operational problems from first conversation to working software in production. Comfortable being judged on measurable manufacturing outcomes, not just code written or designs produced. 
• Adaptability and Field Readiness: Comfortable working in fast-changing manufacturing environments, spending time onsite with operators and engineers, troubleshooting real systems, and balancing speed with safety, reliability, cybersecurity, secure access, maintainability, and uptime requirements in highly controlled production environments. 
• Communication and Influence: Excellent communication and relationship-building skills, with the ability to translate between software engineering, controls, operations, quality, maintenance, product, and business stakeholders as a hands-on individual contributor. 
• High Autonomy, High Accountability: Comfortable operating with minimal process and significant latitude, owning the consequences of what you ship, and building the playbook for software-defined manufacturing as the organization scales. 

You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
• Vehicle discount program for employees and family members and management leases
• Tuition assistance
• Established and active employee resource groups
• Paid time off for individual and team community service 
• A generous schedule of paid holidays, including the week between Christmas and New Year’s Day 
• Paid time off and the option to purchase additional vacation time. 

This position is a salary grade 8 and ranges from $115,000-$192,900.     
 

Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.

For more information on salary and benefits, click here: https://fordcareers.co/GSR

Visa sponsorship is not available for this position.

Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.

This position is hybrid with a requirement to be onsite four or more days per week. #LI-Hybrid

Company: As Ford establishes a wholly owned subsidiary focused on Battery Energy Storage Systems, this role will initially be employed by Ford and is expected to transition to the subsidiary within one year.

#LI-KF2 

#FordEnergy

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.