Dearborn, MI, United States; Palo Alto, CA, United States
Type
Full Time
Responsibilities:
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
Requirements & Skills:
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
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)
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)
