Forward Deployed Engineer, Ford Energy

Ford • Dearborn, MI

29d • $115,000 - $192,900 • Hybrid

About The Position

In this position, while Ford leverages industry-standard platforms for its core ERP, CRM, and MES capabilities, the hardest and high-value problems live at the edge — where customers, operations, physical assets, and data meet software. These are not generic applications for generic business functions. They are custom-built solutions to real operational problems for real customers, built where buying a platform doesn't make sense and where Ford can move faster and smarter by building themselves, accelerated by AI.

Ford is seeking Forward Deployed Engineers to sit at the intersection of engineering and business impact. This is a hands-on individual contributor role for engineers who want to own outcomes, not tickets. Engineers will be directly embedded with internal stakeholders and external customers to understand real operational problems, then designing, building and deploying the software that solves them. You'll work across the full stack: application development, cloud infrastructure on GCP, enterprise data platform for AI scale, integrations with manufacturing and warehousing technology, and automation and tooling that take an idea to a production deploying.

This role is built for engineers who thrive in ambiguity, want direct exposure to the business and customer problems they're solving, and want outsized ownership and accountability over what they ship. You will not be handed a fully specified spec, instead you will be in the room helping define the problem, and then you'll build and deploy the answer.

Ford is building Ford Energy's engineering organization to be AI-native from day one, not an organization that bolts AI onto existing process, but one where intelligent automation, orchestration and applied AI/ML are simply how work gets done.

Requirements

• 5+ years of experience as a software engineer with a strong background in hands-on development, systems architecture, and integrating production systems.
• Comfortable wearing multiple hats (application engineering, cloud infrastructure, and production delivery automation) rather than specializing in only one layer.
• Experience spanning both greenfield development and enterprise platform integrations is highly valued.
• A track record of taking ambiguous, high-stakes problems from first conversation to working software in production and comfortable being judged on outcomes delivered, not just code written.
• Demonstrated experience with AI/ML in production or applied business contexts, combined with a strong practice of using generative AI tools to accelerate development velocity across the SDLC.
• Focused on building smart, automated systems that solve business problems, not just traditional static software.
• Experience with Go, Python, TypeScript, API design, GCP, and modern cloud architectures.
• Experience with infrastructure-as-code, cloud environments, and delivery automation is expected.
• Adaptability: Ability to navigate ambiguous, fast-changing environments and deliver simple, secure solutions under tight timelines.
• Communication: Excellent communication and relationship-building skills, with the ability to partner effectively across diverse technical and business teams as an individual contributor.
• High Autonomy, High Accountability: Comfortable operating with minimal process and significant latitude and comfortable owning the consequences of what you ship. Thrives without a fully-built playbook and builds the playbook as you go.

Nice To Haves

• Prior forward-deployed, field engineering, solutions engineering, or startup founding-engineer experience is a strong plus.
• Experience with MQTT, IoT, edge computing, manufacturing technologies, and enterprise platform integrations (ERP, CRM, MES) is highly valued.
• Familiarity with AI engineering frameworks and intelligent automation patterns is a plus.

Responsibilities

• Build an AI-Native Organization: Help define what “AI-native” actually means for Ford Energy engineering, not as a slogan but as a daily practice. Using generative AI tools and AI-assisted development across the SDLC (design, implementation, testing, infrastructure, delivery), building practical AI/ML capabilities and intelligent automation that solve real operational problems, and setting the example and standards that shape how the team works as it scales. Every use of AI should tie to a clear, measurable case for the value it creates versus its cost; we use AI because it wins on merit, not because it's expected.
• Embed and Solve: Work directly with internal operations, product, and where applicable, customer teams to understand real business and operational problems firsthand, rather than working from a handed-down spec. Translate what you learn into shipped software.
• Own Delivery End to End: Wear multiple hats across the stack: writing application code (Go and related services), designing and operating cloud infrastructure on GCP, and owning the automation, environments, and pipelines that reliably get solutions into production. Success means a deployed solution driving a real outcome, not a handoff between specialized roles.
• Build the Custom Edge: Design, develop, and secure applications, API integrations, and edge systems that fill the gaps around our core ERP, CRM, and MES platforms. Engineer tools purpose-built for Ford Energy's business model and customers rather than generic off-the-shelf capability.
• Architect and Build, Fast: Move fluidly between architecture and hands-on implementation: systems design, API integrations, cloud-native services, IoT/edge workflows, infrastructure-as-code, and production-ready software with a bias toward simple, secure, and reliable solutions deployed quickly.
• Turn Ambiguity into Deployed Software: Convert evolving, sometimes loosely-defined operational and business needs into working systems in the field, using sound engineering judgment and AI-assisted development to move fast without cutting corners on security or reliability.
• Partner Relentlessly: Build direct working relationships with product, operations, and business stakeholders, and customers where relevant, to ensure what gets built actually solves the problem in front of it, not just the problem as originally described.
• Uphold the Standard: Contribute to engineering standards through high-quality design and code, participate in architecture and code reviews (including AI/ML integration patterns and cloud/infrastructure approaches), and share technical knowledge with peers as the team scales.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed