Apply here https://forms.gle/RkFTrJNnogrAcirg7
From Michael Bunsen
I am happy to announce that we have a paid internship position open to assist with the software development of the AMI Data Platform!Looking for candidates that have experience with Python and web development who want experience creating a production application. In particular, an application that operationalizes machine learning pipelines to scale our ability understand the status of insects!Please pass around! We will start responding to applicants on July 15th.
Duration: 6-8 months, paid full-time.
● Flexible start date.
● Location: Preferred in-person at Mila in Montreal, Canada. Partial or wholly virtual
participation may be possible.
● Required minimal expertise: Undergraduate or graduate studies in computer science,
computer or software engineering, data science, or other related disciplines
Deadline to apply: July 15th 2024, 11.59pm AoE
Preferred start date: September 1st 2024 (to be discussed)
Submit your application here
Summary:
Six to eight months, full-time internship at Mila in machine learning engineering focusing on
biodiversity monitoring.
Role Responsibilities:
● Collaborate on an API driven web platform project, written in Python, that acts as a data
catalog and end point to machine learning pipelines.
● Contribute to code development, application architecture, tests and process automation.
● Investigate performance issues relating to the platform and its components.
● Assist in operationalizing machine learning pipelines.
● Participate in data analysis and discussions about the machine learning elements of the
project.
● Help writing documentation and possibly scientific content, such as submissions to
academic journals or conferences.
Logistics:
● Duration: 6-8 months, paid full-time.
● Flexible start date.
● Location: Preferred in-person at Mila in Montreal, Canada. Partial or wholly virtual
participation may be possible.
● Required minimal expertise: Undergraduate or graduate studies in computer science,
computer or software engineering, data science, or other related disciplines
Candidate Profile:
● An intermediate level of expertise in Python for object oriented programming (see
questions below).
● An intermediate level of expertise with web development.
● An intermediate level of experience and knowledge concerning object oriented design
patterns.
● Experience in at least two of the following areas:
○ Working with remote servers and instances (command line and ssh connections
only).
○ Continuous integration tools.
○ Django framework.
● Additional assets:
○ Knowledge of machine learning and/or data science principles.
○ Experience with the Pytorch library
Please go to the form* to submit your cv and short responses:
● A link to your cv online, or upload pdf.
● Provide your response to the following 4 questions (total maximum 1 page):
○ Can you describe a project/application you’ve completed using Python? What
was the challenge, and how did your solution address it?
○ Have you used APIs of any web services related to machine learning?
(HuggingFace, Roboflow, Databricks)
○ Can you expand on and describe the differences between code that will only be
executed locally and/or that is of an experimental nature, versus code that will be
deployed and maintained over time? Think in terms of requirements, best
practices and constraints.
○ Have you contributed to any open-source Python projects? Please describe your
contribution and learning from the experience.
*When submitting your materials online, it won’t work from an incognito mode browser.
Thanks! Prospective interviewees will be contacted by the 19th of July the latest. If you do not
hear back, thank you for your interest… not all applicants will be contacted.
About the project:
We are designing and developing a series of tools that will provide end-to-end solutions for
automated, reliable insect monitoring using computer vision and other techniques from the latest
research in machine learning. Weather-ready hardware devices have been developed by
multiple institutions to autonomously photograph insects. Our group is tasked with adding
intelligence to these devices by automatically detecting and identifying species and providing a
standardized pipeline to validate, share and analyze results. This requires software both
on-device and in the cloud, with user interfaces designed for both biologists and machine
learning engineers. Our primary application is a web-based platform that orchestrates batch ML
processing, hardware registration and collaborative validation of the results in an easy-to-use
workflow