Machine Learning, AI Developer

Ford Motor Company

Machine Learning and AI Developer overseeing AI engineering and vendor tuning for Ford's Telemetry and Observability Platform. Collaborating on AI systems and improving engine performance.

Posted 5/2/2026full-timeDearborn • Missouri • 🇺🇸 United StatesMid-LevelSenior💰 $85,400 - $192,900 per yearWebsite

Tech Stack

Tools & technologies

CloudGoogle Cloud PlatformPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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 insourcing 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.

Requirements

What you’ll need

5+ years of professional experience in machine learning engineering, AI systems development, or applied AI research
3+ years Hands-on experience fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms
2+ years of experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling
4+ years of Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments
3+ years of experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics
5+ years of Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP
4+ years Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns
Strong written and verbal communication skills; ability to translate technical AI concepts for non-technical executive stakeholders.

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning engineeringAI systems developmentfine-tuning LLMsPythonHugging FacePyTorchTensorFlowMLflowmodel evaluation metricsMLOps practices

Soft Skills

strong written communicationstrong verbal communicationability to translate technical concepts