Data Scientist, NLP, AI Engineer

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Ford Motor Company

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1903

📦 Logistics

💼 Consulting

📣 Marketing

💰 Post-IPO Debt on 2023-08

Logistics • Consulting • Marketing

Ford Motor Company is a globally renowned automotive company based in the United States, established by Henry Ford. The company is committed to building a better world where every individual has the freedom to move and follow their dreams. Ford is dedicated to innovation, with a focus on services, experiences, and software alongside its traditional vehicle manufacturing. The company is actively involved in sustainability initiatives and aims to meet ambitious environmental targets. Ford values service, community impact, and strives to combine business success with social and environmental responsibility. With a rich history of over 121 years, Ford continues to adapt and lead in the evolving automotive landscape.

📋 Description

• Design, prototype, evaluate, and productionize LLM-powered applications for enterprise and customer-facing use cases • Develop prompt strategies, structured-output workflows, model routing, context-management approaches, and fine-tuning or adaptation methods when appropriate • Create rigorous LLM evaluation frameworks covering task quality, factuality, relevance, robustness, latency, cost, safety, and user experience • Explore and implement agentic AI patterns such as planning, tool and function calling, memory, reflection, human-in-the-loop approvals, and multi-step workflow execution • Develop and deploy scalable AI and machine learning solutions on GCP using Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, and related data and AI services • Build reusable Python components, APIs, experimentation pipelines, and data products integrating with enterprise platforms and applications • Create and maintain data pipelines preparing structured and unstructured data for modeling, experimentation, evaluation, and production use • Perform exploratory analysis, feature engineering, statistical modeling, and machine learning to support broader data science needs • Implement observability, monitoring, guardrails, automated testing, and feedback loops to improve model and application performance after deployment • Communicate technical findings, tradeoffs, risks, and recommendations to technical and non-technical stakeholders • Stay current with emerging generative AI methods and translate promising research into practical business value

🎯 Requirements

• Bachelor's degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience • Master's degree or Ph.D. in a relevant quantitative or technical discipline • 2+ years of professional experience in data science, machine learning, applied AI, natural language processing, or a related technical role • 5+ years of experience designing or deploying agentic AI systems, including tool-using agents, graph-based workflows, multi-agent collaboration, or human-in-the-loop controls • Strong programming skills in Python and proficiency with SQL; experience writing maintainable, testable, production-quality code • Hands-on experience developing applications with commercial or open-source LLMs • Experience with prompt engineering, LLM evaluation, model integration, and core NLP concepts • Experience with GCP data and AI services, particularly Vertex AI and BigQuery, or comparable major-cloud experience with ability to transition to GCP • Experience with Git, APIs, containers, CI/CD, identity and access management, and production monitoring • Ability to translate ambiguous business needs into measurable technical objectives and deliver iteratively in a cross-functional environment • Strong analytical, problem-solving, documentation, and communication skills • Preferred: hands-on experience with Vertex AI Model Garden, Generative AI Studio, custom training, endpoints, pipelines, evaluation, and monitoring • Preferred: experience with BigQuery, BigQuery ML, Dataflow, or related GCP services • Preferred: experience with fine-tuning, parameter-efficient adaptation, synthetic-data generation, distillation, model serving, or optimization • Preferred: experience developing evaluation datasets, automated evaluators, adversarial tests, red-team scenarios, and regression test suites for generative AI • Preferred: knowledge of LLMOps and MLOps, versioning, experiment tracking, monitoring, scalable inference, and cost optimization • Preferred: experience delivering AI solutions in regulated, safety-conscious, or large-enterprise environments