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Founding Machine Learning Engineer Jobs in Alberta

TELUS

Senior Data Analyst (L4)

Edmonton, AB · On-site

GCP Professional Machine Learning Engineer Certification * Working knowledge of leveraging Claude in the workflows * Experience with Google Vertex AI or Kubeflow for ML orchestration * Background in ...

TELUS

Senior Data Analyst (L4)

Calgary, AB · On-site

GCP Professional Machine Learning Engineer Certification * Working knowledge of leveraging Claude in the workflows * Experience with Google Vertex AI or Kubeflow for ML orchestration * Background in ...

TELUS

Staff Applied AI/ML Scientist

Edmonton, AB

Expertise in machine learning frameworks such as TensorFlow, Pytorch, and Keras * Strong understanding of software and AI development lifecycles, with experience in DevOps and MLOps practices

TELUS

Staff Applied AI/ML Scientist

Calgary, AB · On-site

Expertise in machine learning frameworks such as TensorFlow, Pytorch, and Keras * Strong understanding of software and AI development lifecycles, with experience in DevOps and MLOps practices

Royal Bank of Canada

Staff AI Engineer

Calgary, AB

... and machine learning, has access to rich and massive datasets, and offers the computational ... Collaborating with engineers and AI researchers to architect, build, integrate and deploy AI ...

Data Elephant Inc

Senior AI / ML Engineer

Calgary, AB · On-site

Implement and operationalize machine learning models (training, deployment, monitoring) * Work with ... Strong programming and querying skills (Python, SQL, PySpark) * Experience building production ...

TerraSense Analytics Ltd

MLOps Developer III

Calgary, AB · On-site +1

CA$10/hr

Terra Sense Analytics is looking for a MLOps Developer We truly believe that it's our team that ... of machine learning algorithms. * Serve as a technical reference point, coaching staff and ...

AppDirect

Software Developer

Calgary, AB

Exposure to machine learning model integration or building AI-powered product features is an asset ... engineering roles where asking the right questions shapes outcomes. At AppDirect, we believe that ...

PwC

AI & Advanced Analytics, Senior Manager

Calgary, AB

CA$168K - CA$218K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Royal Bank of Canada

Principal AI Engineer

Calgary, AB · On-site

... in machine learning, has access to rich and massive datasets, and offers the computational ... Apply rigorous engineering practices, including code quality, automated testing, CI/CD, performance ...

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Founding Machine Learning Engineer information

What is a Founding Machine Learning Engineer?

A Founding Machine Learning Engineer is one of the first technical team members at a startup who specializes in designing, building, and deploying machine learning systems. This role involves working closely with the founders to set the technical direction, build core AI products, and establish best practices for data and model development. In addition to hands-on coding and experimentation, a Founding Machine Learning Engineer often influences product decisions and helps shape the company's engineering culture. The role typically requires a blend of deep technical expertise, startup agility, and a willingness to tackle both high-level strategy and low-level engineering tasks.

What are some unique challenges and expectations for a Founding Machine Learning Engineer in an early-stage startup?

As a Founding Machine Learning Engineer, you'll face the unique challenge of building the company's machine learning infrastructure from the ground up, often with limited resources and rapidly evolving requirements. You'll be expected to wear many hats, from designing and deploying models to setting up data pipelines and collaborating closely with product and engineering teams. Your role will also involve making critical decisions about technology stacks and best practices that will shape the company's technical direction. Additionally, you'll have significant influence on the company's culture and have ample opportunities for growth as the team expands.

What are the key skills and qualifications needed to thrive as a Founding Machine Learning Engineer, and why are they important?

To thrive as a Founding Machine Learning Engineer, you need deep expertise in machine learning algorithms, software engineering, and data science, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and experience deploying ML models in production are typically required. Strong problem-solving abilities, entrepreneurial mindset, and excellent communication skills set standout candidates apart. These skills and qualities are vital for driving innovation, building scalable solutions from scratch, and collaborating within a fast-paced startup environment.
What are popular job titles related to Founding Machine Learning Engineer jobs in Alberta? For Founding Machine Learning Engineer jobs in Alberta, the most frequently searched job titles are:
What job categories do people searching Founding Machine Learning Engineer jobs in Alberta look for? The top searched job categories for Founding Machine Learning Engineer jobs in Alberta are:
What cities in Alberta are hiring for Founding Machine Learning Engineer jobs? Cities in Alberta with the most Founding Machine Learning Engineer job openings:
Founding Machine Learning Engineer jobs near you

Senior Data Analyst (L4)

TELUS

Edmonton, AB • On-site

Other

Posted 4 days ago

TELUS rating

8.0

Company rating: 8.0 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

18th of 79 rated telecommunications companies

Job description

Description

The Opportunity

We are looking for a proactive, growth-minded Senior Data Analyst / ML Engineer with a minimum of 5 years of experience in analyzing high-volume data and delivering strategic insights. This is a unique "growth-track" role: you will start by mastering our data landscape through advanced dashboarding and telemetry analysis, then rapidly transition into building and maintaining the predictive models that drive our customer success.

If you are someone who isn't just looking for a ticket to solve, but wants to understand the why behind the numbers to proactively prevent customer issues, you'll fit right in.

The Roadmap

  • Phase 1 (Domain Mastery & KPI Strategy): You'll dive deep into our telemetry and customer profile data. You will define the Key Performance Indicators (KPIs) that matter most and build high-impact Tableau dashboards to track them
  • Phase 2 (ML Innovation & Production): Once you've mastered the domain, you will lead the transition into predictive modeling. You will own the feature engineering process and deploy models that proactively solve customer issues

What You'll Do

  • Dashboarding & Domain Learning: Initially, you will focus on building interactive and insightful Tableau dashboards. This is your foundation to learn our domain knowledge, understand customer behavior, and identify patterns in our telemetry data
  • Feature Engineering: Architect and transform raw telemetry and customer profile data into high-signal features. You will build the data pipelines that serve as the foundation for all ML initiatives
  • ML Development & Lifecycle Management: Develop and deploy ML models (churn prediction, anomaly detection, etc.). You are responsible for maintaining models in production, including setting up automated retraining pipelines to ensure accuracy as data evolves
  • Proactive Problem Solving: Use your technical expertise to identify potential customer friction points before they become issues, moving the company from a reactive to a proactive stance
  • KPI Development & Stakeholder Presentation: Collaborate with leaders to define critical business KPIs. You will act as a data storyteller, presenting key insights to executive stakeholders and translating complex ML/Data trends into clear, strategic recommendations
Qualifications

What You Bring

  • SQL Mastery: You write clean, efficient, and complex queries at an expert level. This is the core of how you interact with our data
  • BI & Dashboarding Expertise: Proficiency in Tableau, creating interactive dashboards that drive action and help you (and the team) rapidly acquire domain knowledge
  • GCP Ecosystem: Strong experience with Google Cloud Platform, specifically BigQuery and its integration with ML tools
  • ML Ops Experience: Practical experience managing the production lifecycle, monitoring, versioning, and ensuring timely model retraining
  • The "Proactive" Edge: A strong desire to learn the business domain and grow with the company. You don't wait for instructions; you identify opportunities and solve problems before they escalate
  • Excellent Communication Skills: A proven track record of analyzing high-volume data and presenting key findings to senior-level audiences. You can explain complex technical concepts to non-technical stakeholders with ease

Preferred Qualifications (Good to Have)

  • GCP Professional Machine Learning Engineer Certification
  • Working knowledge of leveraging Claude in the workflows
  • Experience with Google Vertex AI or Kubeflow for ML orchestration
  • Background in analyzing high-volume telemetry or IoT data

Technical Stack

  • Languages: SQL (Expert), Python
  • Data Warehouse: Google BigQuery
  • Cloud Infrastructure: Google Cloud Platform (GCP)
  • Visualization: Tableau, Looker, Etc
  • ML Tools: Scikit-learn, TensorFlow/PyTorch, Vertex AI (GCP) , Claude (Anthropic)

Apply