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Founding Machine Learning Engineer Jobs in Edmonton, AB

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 ...

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

Research activities span sensors, controls, communications, analytics (including machine learning ... The Sr. Research Engineer will contribute to the research, development, implementation, and ...

Sr. Software Developer PulseMedica is seeking a Senior Software Developer to help build the next ... Experience with machine learning. * Experience with 3D Rendering. * Experience with embedded SW ...

... machine learning models in a production environment. * Expertise in Python data science libraries like Pandas, matplotlib, NumPy, and Scikit-Learn. * Proficiency in programming languages such as ...

Train machine learning systems using data annotation and benchmarking techniques. Help your ... Prior exposure to a statistical or scientific programming environment is helpful. If you don't have ...

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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 job categories do people searching Founding Machine Learning Engineer jobs in Edmonton, AB look for? The top searched job categories for Founding Machine Learning Engineer jobs in Edmonton, AB are:

MLOps Engineer (Energy) - MLEEAS

NavitasPartners

Edmonton, AB

$30/hr

Other

Posted 5 days ago


Job description

Job Title : MLOps Engineer (Energy)Industry

Energy & Utilities

Position Overview

The MLOps Engineer will establish and manage scalable ML infrastructure, ensuring efficient deployment, monitoring, governance, and lifecycle management of machine learning solutions.

Responsibilities
  • Build ML deployment pipelines.
  • Implement CI/CD for ML systems.
  • Automate model retraining workflows.
  • Monitor model performance and drift.
  • Manage ML infrastructure and environments.
  • Ensure governance and compliance requirements.
Required Skills
  • MLOps
  • Kubernetes
  • Docker
  • MLflow
  • Databricks
  • Azure ML / SageMaker / Vertex AI
  • GitHub Actions
  • Terraform
Preferred Skills
  • Energy Forecasting Models
  • Asset Optimization Models
  • Real-Time Monitoring Systems
Mandatory Experience
  • 5+ years in MLOps or ML Platform Engineering.
  • Must have prior Energy industry experience.


For more details reach at resumes@navitassols.com