Top Machine Learning Jobs in Calgary

As a Senior Engineering Manager for ML & AI, you'll lead Data Science and ML initiatives, mentoring team members, and collaborating across R&D to drive strategic AI solutions that impact the business.

As a PhD AI Research Intern, you'll collaborate with ML researchers to design experiments and evaluate AI models, contributing to publications while gaining practical experience in AI and machine learning applications.

Lead and manage a team of software engineers to develop innovative hardware, software, and AI solutions for physical operations, ensuring customer satisfaction and team growth.

The AI Engineer will develop and optimize AI solutions, focusing on LLMs, manage datasets, and collaborate with cross-functional teams to align AI/ML solutions with business objectives while ensuring data quality and performance.

The Senior Lead AI Solutions Consultant at Braze drives revenue by enhancing the understanding of the BrazeAI tools, engaging with enterprise customers, leading workshops, and collaborating across teams to improve products and customer solutions.

The AI Business Analyst will optimize Dropbox's support systems using AI, analyze performance, and guide AI project prioritization to improve customer outcomes.

Lead teams in developing computer vision and machine learning solutions for AI dash cameras. Oversee model lifecycle and ensure reliable features by collaborating across teams.

Define and execute the product vision for leveraging data science at DraftKings, focusing on personalization and customer engagement through collaboration across multiple teams.

Lead the vision and development of Machine Learning capabilities at Affirm, managing model lifecycles and fostering AI adoption across products and teams.

Lead and develop the Data Loss Prevention Team, establishing strategy and overseeing implementation of DLP tools to enhance security and efficiency at Coinbase.

As a Staff Machine Learning Engineer at Coinbase, you will define technical strategies, design ML models for risk assessment, create real-time ML pipelines, and deploy models at scale to optimize operations and enhance user security.

Lead the Payments Risk engineering team at Coinbase, develop strategies, mentor team members, ensure code quality, and collaborate with cross-functional teams to manage financial risks.

Lead data and AI transformation programs, partnering with executives to define strategies and deliver modern data platforms and solutions, while driving business growth and ensuring excellence.

As a Principal MLOps Developer, you'll build scalable ML training pipelines, optimize training systems, and collaborate with AI researchers to innovate within the AEC industry.

The Emissions Scientist will develop statistical methods for emissions data analysis, incorporating various models and collaborating with teams while presenting results to diverse audiences.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

The Senior Staff Machine Learning Engineer will lead the design and scaling of advanced ML systems, mentor engineers, and define technical strategy across Affirm.

Lead the design and deployment of scalable ML and data pipelines. Collaborate across teams, optimize infrastructure, and mentor engineers.

As a Machine Learning Engineer, you will develop and implement machine learning algorithms, train models, collaborate with product and engineering teams, and guide junior engineers.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Lead a team of data scientists and AI engineers to deliver AI-driven solutions and drive business impact through innovation and collaboration.

Lead the development of AI-driven security workflows, integrate machine learning models, establish data pipelines, and mentor engineers.

As a Product Manager for Risk, you'll design fraud prevention tools, manage cross-functional teams, and improve fraud detection methods.

Design and build data architectures, oversee ETL/ELT pipelines, collaborate with stakeholders to optimize data usage, and implement data quality frameworks.

Contributors will provide expertise to train AI models, evaluate outputs, and develop prompts. Requires strong writing and analytical skills.