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Machine Learning Petroleum Engineer Jobs in Toronto, ON

Job Summary As a Principal Machine Learning Engineer, you will design, build, deploy, and scale machine learning and generative AI systems that power real-world products. You will work closely in AI ...

Join EvenUp as a Staff Machine Learning Engineer and help set the technical direction for how machine learning powers Piai™, our proprietary claims-intelligence platform. This is a technical ...

What you'll do As a machine learning engineer, you will be responsible for analyzing opportunities, proposing ideas, training & evaluating ML models, running experiments, and deploying everything to ...

We're in search of an exceptional ML engineer with extensive experience in suggesting, exploring ... Experience fine-tuning LLMs for specific learning tasks Experience deploying full-stack features to ...

We are looking for a Sr. Machine Learning Engineer to help translate raw data into meaningful insights that drive strategic decision-making. The Opportunity Summary We are seeking an experienced ...

Senior Machine Learning Engineer At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise ...

Showing results 21-40

Machine Learning Petroleum Engineer information

How does a machine learning petroleum engineer typically collaborate with geoscientists and drilling teams to optimize oil and gas production?

A Machine Learning Petroleum Engineer works closely with geoscientists and drilling teams by integrating data-driven models into exploration and production workflows. They analyze geological, seismic, and operational data to develop predictive algorithms that identify optimal drilling locations, forecast reservoir performance, and improve recovery rates. Regular collaboration involves translating complex data insights into actionable recommendations that guide drilling strategies and inform real-time decisions, ensuring all teams are aligned to maximize efficiency and safety. This multidisciplinary approach fosters continuous learning and innovation across teams.

What is the difference between Machine Learning Petroleum Engineer vs Reservoir Engineer?

AspectMachine Learning Petroleum EngineerReservoir Engineer
Required CredentialsBachelor's/Master's in Petroleum Engineering, Data Science, or related fields; knowledge of machine learningBachelor's/Master's in Petroleum Engineering or Geosciences; strong understanding of reservoir simulation
Work EnvironmentData analysis, modeling, software development in oil & gas companiesReservoir modeling, field development planning in oil & gas operations
Industry UsageApplying machine learning to optimize extraction, predict reservoir behaviorEstimating reservoir properties, managing production strategies

The Machine Learning Petroleum Engineer focuses on integrating data science and machine learning techniques to optimize oil extraction processes, while the Reservoir Engineer specializes in modeling and managing subsurface reservoirs to maximize recovery. Both roles are vital in the oil & gas industry but differ in their core skills and daily tasks.

What is a machine learning petroleum engineer?

A Machine Learning Petroleum Engineer is a specialist who combines expertise in petroleum engineering with machine learning and data science techniques. They use advanced algorithms and data analytics to optimize oil and gas exploration, drilling, production, and reservoir management. Their work helps improve decision-making, reduce operational costs, and increase efficiency by analyzing large datasets from various sources such as sensors, seismic data, and production logs. These professionals often work closely with geoscientists, data engineers, and other stakeholders in the energy sector.

What are the key skills and qualifications needed to thrive as a machine learning petroleum engineer?

To thrive as a Machine Learning Petroleum Engineer, you need a strong background in petroleum engineering, programming (such as Python or R), and applied machine learning, usually supported by a relevant engineering degree. Familiarity with data analysis platforms, machine learning frameworks (like TensorFlow or Scikit-learn), and petroleum industry software (such as Petrel or Eclipse) is essential. Strong analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for integrating technical insights with business goals. These competencies enable the effective application of data-driven solutions to optimize exploration, production, and operational efficiency in the energy sector.

What are popular job titles related to Machine Learning Petroleum Engineer jobs in Toronto, ON?

For Machine Learning Petroleum Engineer jobs in Toronto, ON, the most frequently searched job titles are:

What job categories do people searching Machine Learning Petroleum Engineer jobs in Toronto, ON look for?

The top searched job categories for Machine Learning Petroleum Engineer jobs in Toronto, ON are:

Infographic showing various Machine Learning Petroleum Engineer job openings in Toronto, ON as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Principal Machine Learning Engineer

Equinix

Toronto, ON • Remote

Full-time

Medical, Life, Retirement, PTO

Posted 4 days ago


Equinix rating

9.1

Company rating: 9.1 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

5th of 253 rated facilities management


Job description

Who are we?

Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet. 

A place where tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective.Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

As a Principal Machine Learning Engineer, you will design, build, deploy, and scale machine learning and generative AI systems that power real-world products. You will work closely in AI Sidekick team and business teams to translate advanced ML and LLM capabilities into reliable, production‑grade solutions across multi‑cloud environments including GCP, AWS, and Azure.

This role blends applied machine learning, software engineering, and MLOps, with a strong focus on building robust, scalable systems rather than purely academic research.


Responsibilities

  • Design, develop, and deploy machine learning and Large Language Model (LLM)–based solutions for production use cases

  • Collaborate with Generative AI Center of Excellence leaders and business stakeholders to evaluate buy vs. build decisions for generative AI applications

  • Develop end-to-end ML pipelines, covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring

  • Architect and implement LLM-powered systems that integrate agents and services across multiple cloud platforms into a unified solution

  • Optimize ML workflows for performance, scalability, reliability, and cost efficiency in cloud environments (GCP, Azure, AWS)

  • Implement and maintain MLOps best practices, including CI/CD, model versioning, experiment tracking, and automated retraining

  • Work extensively with deep learning frameworks such as PyTorch and TensorFlow

  • Containerize ML services and deploy them using Docker, Kubernetes, App Engine, or virtual machines

  • Apply strong knowledge of NLP fundamentals, including transformers, attention mechanisms, embeddings, and text preprocessing

  • Deploy and manage models in production, conduct A/B testing, and measure performance improvements using statistical methods

  • Develop features, run experiments, analyze results, and translate insights into actionable improvements

  • Build and deploy classical ML models (regression, classification, clustering), NLP applications (sentiment analysis, summarization, Q&A, chatbots, information retrieval), and computer vision solutions (image classification, object detection, segmentation using models such as YOLOv7, DDRNet, RFTM with datasets like COCO and Cityscapes)

 
Qualifications

  • PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years of experience in Machine Learning, Computer Science, Data Science, or a related field

  • Strong proficiency in Python for machine learning and production systems

  • Solid understanding of software engineering fundamentals, system design, and design patterns

  • Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)

  • Experience building and deploying production-grade ML systems

  • Strong communication skills with the ability to explain technical concepts and results to both technical and non-technical stakeholders

  • Excellent time management, collaboration, and organizational skills

The targeted pay range for this position in the following location is / locations are:

Canada - Toronto Office TRO : 154,000 - 232,000 CAD / Annual

Our pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location.The pay range shown is based on our compensation structure in place at the time of posting and may be updated periodically based on business needs. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training.

The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits. Employees are eligible for bonus, and equity may be offered depending on the position.

Equinix Benefits

As an employee, you become important to Equinix’s success. We ensure all your benefits are in line with our core values: competitive, inclusive, sustainable, connected and efficient. We keep them competitive within the current marketplace to ensure we’re providing you with the best package possible. So, wherever you are in your career and life, you’ll be able to enhance your experience and bring your whole self to work.

Employee Assistance Program: An Employee Assistance program is available to all employees.

Canada Core Benefits: - Insurance: You may enroll in healthcare coverage that is designed to complement the provincial healthcare system, along with life, disability and optional benefit plans that are designed for you and your eligible family members. - Retirement: You may also enroll in Equinix-sponsored retirement or savings plans: Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax-Free Savings Plan (TSFA). - Vacation and Paid Holidays: Equinix offers both vacation and personal time, along with various paid holidays for you to rest and recharge. Eligibility requirements apply to some benefits. Benefits are subject to specific plan or program terms, and to change at Equinix discretion.

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability.  If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer.  All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law. 

We use artificial intelligence in our hiring process. Learn more here.

This posting is a new position within our organization.

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