About the role
Job Description
Job Title: Junior Machine Learning Engineer
Working Pattern: Full time
Working location: Indianapolis, IN. (Hybrid: 3 days on site)
Relocation assistance will be provided if applicable.
Why Rolls-Royce?
At Rolls-Royce we are proud to be a business that has truly helped to shape the modern world and are committed to always being a force for progress; powering, protecting and connecting people everywhere.
By joining Rolls-Royce, you’ll have the opportunity to create world-class power and propulsion solutions, pushing your problem-solving and ingenuity, to deliver real impact that puts safety first.
You’ll be given responsibility and the opportunity to grow in our high-performance culture. This is Infinite Potential. And it’s your chance to really shine and contribute to one of the world’s most recognized brands and a beacon for engineering excellence
Position Summary
As a Rolls-Royce Product Definition Engineer your main task will be to produce and (where adequate capability has been demonstrated) check Component definitions (such as Model Based Definitions - MBDs) in line with Engineering Standards to meet design intent, satisfying fit, form and function of the component while optimizing for manufacture.
What you will be doing
We’re looking for a Junior Machine Learning Engineer to join our growing team. In this role, you will tackle exciting challenges in AI/ML, software development, and Data Science. You’ll be part of a multi-disciplinary team, working together to tackle technical challenges in a stimulating and collaborative environment. In this role, your focus will be on building and deploying tools to deliver insights on Rolls-Royce products.
Work with IT, and Rolls-Royce engineering teams address key opportunities for application of digital technologies providing optimal impact and value.
Understand, clean, and analyze large datasets from engine sensors, test rigs, and fleet operations.
Build, train, and validate machine learning models for tasks such as searching engineering knowledge, anomaly research, production support, and predictive maintenance.
Document your work clearly and present findings to both technical and non-tec…
