Machine Learning Engineer

Viridien

$90K — $120K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Degree in Computer Science, Mathematics, Physics, Electrical Engineering, or related field
  • Proficiency in programming with languages like C, C++, R, Java, or Python
  • Strong project experience in Machine Learning and AI
  • Good understanding of Large Language Model technologies
  • Experience with deep learning frameworks such as TensorFlow or PyTorch
  • Ability to manage project deadlines and workloads effectively
  • Effective communication skills for stakeholder engagement

Responsibilities

  • Design and develop machine learning systems
  • Collaborate with teams to create AI solutions
  • Research and implement ML algorithms and tools
  • Build machine learning applications based on specified requirements
  • Support the deployment of machine learning models
  • Plan and manage data analysis workflows
  • Create visualizations to convey data trends and insights

Benefits

  • Work in a cutting-edge technology environment
  • Opportunities for collaboration and idea generation
  • Contribute to sustainable and responsible solutions
  • Support for continuous learning and professional growth
  • Inclusive workplace committed to diversity and equal opportunity
Full Job Description
Machine Learning Engineer

Calgary, AB, Canada

Full-time

Job Description

Viridien is looking for a Machine Learning (ML) Engineer to help us create artificial intelligence systems and tools. Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. Strong skills in statistics and programming, as well as knowledge of data science and software engineering. As an integral member of our team, we highly encourage the contribution of ideas and drive in the generation of new concepts to maintain our outstanding leadership position for technology and service delivery in the marketplace.

Must be legally authorized to work in Canada.

Qualifications

Preferred Education

Degree in Computer Science, Mathematics, Physics, Electrical Engineering, or other related technical disciplines.

Key Skills & Competencies
  • Passion and aptitude for programming and technology
  • Enthusiasm for analytical and problem-solving challenges
  • Strong enterprise project experience with Machine Learning and AI
  • Strong programming skills within one or more of these development languages - C / C++ / R / Java / Python
  • Good experience with Large Language Model technologies
  • Experience within Data Engineering/Data Structuring
  • Experience creating Machine Learning Algorithms and/or Libraries.
  • Proven experience with deep learning frameworks and usage of DL libraries (TensorFlow/PyTorch)
  • Proficiency to design, build, test, and support innovative solutions.
  • Ability to define and manage project deadlines and balance workloads across a wide variety of projects.
  • Effective communication skills to keep all stakeholders regularly informed on progress.
  • Drive to innovate and have fun through collaboration and generation of ideas which lead to enhancements of our workflows.
  • Enthusiastic attitude towards learning and flexibility to adapt to new challenges or changes in direction.


Other Skills/Experience:
  • Data Visualization
  • Predictive Analysis
  • Statistical Modeling
  • Data Mining
  • Clustering & Classification
  • Data Analytics
  • Quantitative Analysis
  • Web Scraping
  • Model Development


Responsibilities:
  • Design machine learning systems
  • Collaborate with stakeholders and technology team to efficiently develop AI solutions.
  • Research and implement appropriate ML algorithms and tools.
  • Develop machine learning applications according to requirements.
  • Provide support to achieve successfully deployed models at conclusion of projects.
  • Plan and manage data analysis workflows.
  • Create charts, graphs, maps, and data visualization tools to provide an accessible way to see/understand trends, patterns, outliers, in data.
  • Select appropriate datasets and data representation methods.
  • Run machine learning tests and experiments.
  • Train and retrain systems when necessary.
  • Extend existing ML libraries and frameworks.


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