DIA Maching Lrng Eng

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.

The future of smart mobility hinges on the intelligent application of data, metrics, and analytics. As part of our Global Data Insight & Analytics (GDI&A) team, you'll play a pivotal role. We serve as Ford's trusted advisers, providing clear insights into business conditions, customer needs, and the competitive environment. Our work empowers key decision-makers to act decisively and positively. By leveraging your expertise in data and analytics, you can contribute to timely, evidence-based decision-making.

Ford's GDI&A department is on the hunt for talented individuals skilled in Machine Learning, Big Data, Statistics, Econometrics, and Optimization. Our mission is to foster evidence-based decisions by unlocking insights from data. Our projects span various domains, including Connected Vehicle, Smart Mobility, Operations, Manufacturing, Supply Chain, Logistics, and Warranty Analytics.

We're looking for exceptional Machine Learning and AI scientists who are eager to engage in all project stages, from problem identification to model deployment. Ideal candidates are self-motivated, have a strong sense of initiative, and a lifelong passion for learning to navigate the rapidly evolving field of mobility technology. We offer the freedom to conduct original research, select the most suitable methodologies, and tackle world-class machine learning challenges.

This role involves applying AI and ML to innovate in areas such as autonomous vehicles, cybersecurity, customer interaction, and product design. You'll be part of a forward-thinking team dedicated to using AI and ML to create groundbreaking solutions and shape our strategic direction in these fields and beyond.

• Analyze source data and data flows, working with structured and unstructured data (text, audio, images, video, etc.)

• Manipulate high-volume, high-dimensionality data from varying sources to expose and highlight patterns, anomalies, relationships, and trends

• Apply AI and Machine Learning technology to solve complex, real-world problems

• Analyze and visualize diverse sources of data, interpret results in a business context and report results clearly and concisely

• Fulfill problem formulation and ML technique consulting requests in a timely manner

• Communicate and present analytical models to business customers and executive management

• Work collaboratively with different business partners and be able to present results in a clear and concise manner

• Bachelor’s degree in computer science, mathematics, statistics, operations research, or related field 

• Experience with the complete software lifecycle

• 1+ years of experience with delivering and maintaining production software products

• Strong technical writing and oral communication skills

• Expertise in one or more core domains involved in machine learning model deployment, including data engineering, model building, MLOps

• Experience in productionizing generative AI to solve critical business problems

• Preferred Qualifications

• Doctorate in computer science, mathematics, statistics, operations research, or related field AND 1+ year(s) data-science experience (e.g. managing structured and unstructured data, applying statistical techniques and reporting results)

  • OR Master’s degree in computer science, mathematics, statistics, operations research, or related field AND 3+ year(s) data-science 

  • OR bachelor’s degree in computer science, mathematics, statistics, operations research, or related field AND 5+ year(s) data-science experience

• Experience with cloud-based deployments and best practices

• Demonstrated contributions and expertise in one or more of the following AI domains:

  • Natural Language Processing (finetuning and distillation of LLMs, evaluation of LLM-powered applications, deploying models at scale, including development and deployment of LLM-driven AI agents capable of autonomous task execution and tool integration)

  • Computer Vision (anomaly detection / few-shot learning, multi-sensor fusion, object detection and tracking, scene segmentation, motion planning and prediction)

  • Generative AI (fine tuning stable diffusion models, multi-modal large language models, generation in the 3D domain)

  • Physics-informed neural networks (ML for computational fluid dynamics or finite element analysis, point cloud or mesh-based neural networks, PDE surrogate modelling)