Machine Learning and AI Developer

We made history and now we work to transform the future - for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

Ford's Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company's vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You'll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out.

Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams.

In this role...

Ford Motor Company is seeking Machine Learning and AI Developers to join the Connected Vehicle Division in support of the Telemetry and Observability Platform (TOP). In this role, you will be at the center of Ford's AI engineering capability, overseeing vendor fine-tuning operations, designing Ford's internal orchestration layer, and driving measurable improvements in AI engine performance across dealer service, manufacturing, and validation workflows.

Ford Motor Company is seeking GCP Cloud Services Engineers to join the Connected Vehicle Division in support of the Telemetry and Observability Platform (TOP).

What you'll do..

* Support Ford's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation

* Oversee vendor fine-tuning of Google Cloud Vertex AI using Ford proprietary diagnostic data, ensuring compliance with Ford's IP protection requirements and model weight storage architecture

* Design and build Ford's Orchestration Layer. The integration framework that connects external AI engine with other Ford internal AI engines and TOP platform services

* Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops

* Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring clinical accuracy before dealer-facing deployment

* Build internal Ford tooling for model monitoring, drift detection, and retraining triggers within Ford's GCP environment

* Collaborate with Ford's data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality

* Partner with the Ford GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with Ford's IP and security policies

* Contribute to the long-term in sourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks in sufficient detail to enable internal replication

* Represent AI and ML engineering in architecture reviews and vendor technical discussions.

* What you'll do..

* Support Ford's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation

* Oversee vendor fine-tuning of Google Cloud Vertex AI using Ford proprietary diagnostic data, ensuring compliance with Ford's IP protection requirements and model weight storage architecture

* Design and build Ford's Orchestration Layer. The integration framework that connects external AI engine with other Ford internal AI engines and TOP platform services

* Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops

* Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring clinical accuracy before dealer-facing deployment

* Build internal Ford tooling for model monitoring, drift detection, and retraining triggers within Ford's GCP environment

* Collaborate with Ford's data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality

* Partner with the Ford GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with Ford's IP and security policies

* Contribute to the long-term in sourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks in sufficient detail to enable internal replication

* Represent AI and ML engineering in architecture reviews and vendor technical discussions.

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