Now Hiring: Machine Learning Intern (Instrumar)

Join our team! We are looking for a Machine Learning Intern to work with technology company Instrumar.

We’re looking for a talented and enthusiastic Machine Learning (ML) Intern with a solid knowledge of machine learning and experience working with time-series data! This opportunity is a full-time, 4-month paid internship, starting May 2021.

Description

About Instrumar

Instrumar is a Canadian, employee-owned technology company based in St. John’s, Newfoundland.

For more than 40 years, they have been harnessing the technical and creative skills of their team of brilliant engineers, software developers and hardware production specialists to build world-leading solutions to many industrial challenges. Instrumar builds amazingly powerful electromagnetic sensors and software that provide real-time production and quality intelligence for their customers, who are some of the world’s leading manufacturers of synthetic fiber such as nylon and polyester. You probably haven’t heard of them before, but there’s a good chance that you are wearing, walking on, or driving in a product that they have helped manufacture.

Instrumar wants to build on their technical and commercial success by using machine learning (ML) and advanced data analytics to develop more powerful software algorithms so they can provide even deeper understanding for their customers of their quality issues and the underlying causes.

About the Project

This project will explore the detection and prediction of unplanned downtime for production machines using sensor data, leading to measures such as preventative maintenance to reduce unplanned downtime in the future. This is a paid internship that will be undertaken over a four-month period with the potential to be hired afterwards (note: at the discretion of Instrumar). The intern will be reporting to an Amii Lead Scientist and regularly consult with the Instrumar team to share insights and engage in knowledge transfer activities.

Required Skills / Expertise

We’re looking for a talented and enthusiastic individual with solid knowledge of machine learning and experience working with time-series data.

Key Responsibilities:

• Build, train, and evaluate ML models
• Undertake applied research on ML techniques to address the limitations in existing models

Required Qualifications:

• At least one year into a Computing Science / ML graduate program, MSc. or Ph.D
• Research or applied project experience with time-series or sensor data
• Experience working on any of the following topics: time-series modeling, failure detection, outlier detection, and online learning
• Proficient in Python programming language and related libraries and toolkits (e.g.scikit learn, Pandas, Jupyter notebooks)
• A positive attitude towards learning and understanding a new applied domain

Preferred Qualifications:

• Publication record in peer-reviewed academic conferences or relevant journals in machine learning
• Experience working with data engineering workflows
• Candidate who has graduated or is about to graduate

Non-Technical Requirements:

• Interdisciplinary team player enthusiastic about working together to achieve excellence
• Capable of critical and independent thought
• Able to communicate technical concepts clearly and advise on the application of machine intelligence
• Intellectual curiosity and the desire to learn new things, techniques, and technologies

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Get paid for your work
• Work under the mentorship of an Amii Lead Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Build your professional network

If this sounds like the opportunity you've been waiting for, then please don’t wait to apply! To apply, please send your resume and cover letter indicating why you think you'd be a fit for Amii through the listing on Indeed by April 30, 2021.

Amii is proud to be an equal opportunity employer. We are committed to creating a diverse, inclusive and excellent workforce.