Machine Learning Engineer
Toronto, Ontario, Canada
Full Time
Mid Level

A Leader in Automating Intelligent Industrial Operations

At Canvass Analytics, we aim to bring AI into large industrial companies and unlock process data to help make critical business decisions. Canvass’s cutting-edge AI platform can enable industrial companies to optimize complex production processes, improve quality, and reduce energy consumption.  Canvass Analytics’ customers include leading energy, manufacturing, food and agriculture, oil and gas, and metals and mining companies around the globe.

Founded by a team of experienced leaders, Canvass is building an AI-powered analytics platform to transform the Industrial sector. As we accelerate rapidly, we are constantly looking for driven and ambitious innovators to join our team.

Who We Need

We are looking for an experienced Machine Learning Engineer to join our R&D team. The ideal candidate will work with large datasets to do transformations, visualizations and apply artificial intelligence algorithms to solve complex business problems in the Industrial sector.  We are looking for someone who will contribute to the development and deployment of modern AI, Machine Learning, and Deep Learning pipelines to create operational models for predictive analytics. 

 

Your responsibilities as our Machine Learning Engineer will be to: 

  • Create and maintain data pipelines for streaming IoT data

  • Build the infrastructure requires for optimal extraction, transformation and loading data from NoSQL databases and big data technologies

  • Design and implement machine learning algorithms

  • Implement feature engineering and data transformation techniques on large datasets

  • Presenting to and educating customers on product capabilities enabled by Machine Learning and AI 

  • Develop and complete scalable systems that span from data ingestion to model learning and deployment

Who You Are

You are seeking an opportunity to make a difference, bring your insights and your process to a unique product and sector. You want to join a rapidly scaling company that’s at the forefront of AI. 

 

Your skills include: 

  • Experience applying machine learning and deep learning algorithms

  • Strong Python development background

  • Experience with Python ML Libraries such as numpy, pandas, SK Learn, Keras, Tensorflow

  • Able to work with Notebooks to explore data and evaluate concepts

  • Experience with data processing pipelines such as Airflow, Kubeflow

  • Strong comprehension of probability and statistics

  • Ability to create data visualization to understand data and tell a compelling story with data

  • Ability to process and transform large datasets.

  • Deep knowledge and experience in Designing and implementing core modeling components.

  • Excellent interpersonal skills 

Why join Canvass?

Canvass Analytics is using AI to automate operations for the Industrial sector. We are changing the way Industry thinks and uses AI, and it’s getting noticed everywhere.

We’ve been recognized by Network World as a Top 10 Hot AI-Powered IoT Startups and by Canadian Innovation Exchange as a Top 20 Most Innovative Canadian Company. Canvass is backed by Google’s Gradient Ventures. 

What's it like working at Canvass? This is a place where everyone is all in.  From the CEO to the newest hire, we all pitch in where needed and do what it takes.  We are a relatively flat organization where everyone is invited to the table, to listen in and be part of the conversation and decisions.  We appreciate the efforts, innovation and results of our team. 

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Think this job is meant for you but worried you don’t have it all? If you feel you meet 70% of the qualifications listed and you are an innovative team player, express your interest here and we promise to consider your full profile.

 

You can also follow us on Twitter or LinkedIn to learn more about us.

 

Canvass Analytics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.