Machine Learning Engineering
Machine Learning Engineering
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1 Vacancy
Job Description
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 tomorrows 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 youll play a pivotal role. We serve as Fords trusted advisers providing clear insights into business conditions customer needs and the competitive environment. Our work empowers key decisionmakers to act decisively and positively. By leveraging your expertise in data and analytics you can contribute to timely evidencebased decisionmaking.
Fords 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 evidencebased decisions by unlocking insights from data. Our projects span various domains including Connected Vehicle Smart Mobility Operations Manufacturing Supply Chain Logistics and Warranty Analytics.
Were 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 selfmotivated 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 worldclass machine learning challenges.
This role involves applying AI and ML to innovate in areas such as autonomous vehicles cybersecurity customer interaction and product design. Youll be part of a forwardthinking team dedicated to using AI and ML to create groundbreaking solutions and shape our strategic direction in these fields and beyond.
Responsibilities
- Analyze source data and data flows working with structured and unstructured data (text audio images video etc.
- Manipulate highvolume highdimensionality data from varying sources to expose and highlight patterns anomalies relationships and trends
- Apply AI and Machine Learning technology to solve complex realworld 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
Qualifications
- Bachelors degree in computer science mathematics statistics operations research or related field
- 1 years of experience with delivering and maintaining production software products in a team environment (Python experience preferred).
- Experience with cloudbased deployments and best practices (GCP experience preferred).
- Experience with the complete software lifecycle
- 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) datascience experience (e.g. managing structured and unstructured data applying statistical techniques and reporting results)
- OR Masters degree in computer science mathematics statistics operations research or related field AND 3 year(s) datascience
- OR bachelors degree in computer science mathematics statistics operations research or related field AND 5 year(s) datascience experience
Demonstrated contributions and expertise in one or more of the following AI domains:
- Natural Language Processing (finetuning and distillation of LLMs evaluation of LLMpowered applications deploying models at scale)
- Computer Vision (anomaly detection / fewshot learning multisensor fusion object detection and tracking scene segmentation motion planning and prediction)
- Generative AI (fine tuning stable diffusion models multimodal large language models generation in the 3D domain)
- Physicsinformed neural networks (ML for computational fluid dynamics or finite element analysis point cloud or meshbased neural networks PDE surrogate modelling)
Employment Type
Full-Time
