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Machine Learning Engineer Jobs in Quebec (NOW HIRING)

GenAI Engineer

Montreal, QC · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You will develop advanced machine learning algorithms, optimize open-source frameworks, and ... Framework & Pipeline Engineering Build scalable pipelines for data preprocessing, feature ...

Senior Distributed ML Engineer

Montreal, QC · On-site

  • Medical

  • Retirement

  • PTO

We are seeking a senior distributed machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to ...

Position Summary We are looking for a Senior MLOps Engineer to design, build, and maintain the data and machine learning pipelines that power our AI and analytics platforms. This is a deeply hands-on ...

New

AI scientist for Quantum Computing

Montreal, QC · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • PTO

Excellent scientific programming skills, particularly in Python and common machine-learning frameworks. * Experience taking research ideas from initial exploration to robust implementation.

Work with our machine learning engineers to put cutting edge deep learning algorithms in production. * Develop tools and contribute to open source wherever possible. * Adopt problem solving as a way ...

AI scientist for Quantum Computing

Montreal, QC · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • PTO

Excellent scientific programming skills, particularly in Python and common machine-learning frameworks. * Experience taking research ideas from initial exploration to robust implementation.

... Developer Position overview As a Senior ML Developer on the team, you will be responsible for ... Design and implement Machine Learning capabilities that improve Autodesk's RAG platforms * Perform ...

Senior ML Research Developer

Montreal, QC · On-site

  • Medical

  • Retirement

  • PTO

We are seeking a senior machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to solve ...

Sitting at the intersection of data engineering, data curation, and machine learning, you will own the end-to-end pipeline that transforms raw web-scale data into high-signal datasets used to train ...

Help implement continuous integration and deployment (CI/CD) pipelines for machine learning models * Develop and maintain application programming interfaces (APIs) and software development kits (SDKs ...

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

... machine learning? Are you curious about the systems that power information retrieval, index ... As an intern, you'll gain practical experience with regular engineering workflows while also owning ...

Showing results 41-60

Machine Learning Engineer information

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Quebec? The most popular types of Machine Learning Engineer jobs in Quebec are:
What are popular job titles related to Machine Learning Engineer jobs in Quebec? For Machine Learning Engineer jobs in Quebec, the most frequently searched job titles are:
What are popular job titles related to Machine Learning Engineer jobs in QC? For Machine Learning Engineer jobs in QC, the most frequently searched job titles are:
Infographic showing various Machine Learning Engineer job openings in Quebec as of August 2026, with employment types broken down into 80% Full Time, and 20% Contract. Highlights an 100% In-person job distribution.

GenAI Engineer

United Airlines

Montreal, QC • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago


United Airlines rating

7.9

Company rating: 7.9 out of 10

Based on 342 frontline employees who took The Breakroom Quiz

7th of 26 rated airlines


Job description

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired bya collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizationsunlock the value of technology and build a more sustainable, more inclusive world.

Role Overview
  • We are seeking a GenAI & AI/ML Framework Specialist to design, build, and scale next-generation artificial intelligence solutions.
  • You will develop advanced machine learning algorithms, optimize open-source frameworks, and implement Generative AI architectures into enterprise applications.
Key Responsibilities
  • GenAI & Model Development
    Design and deploy Generative AI solutions using Large Language Models (LLMs) and diffusion models.
    Implement optimization techniques including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
    Develop predictive models by selecting, training, and tuning traditional machine learning and deep learning algorithms.
  • Framework & Pipeline Engineering
    Build scalable pipelines for data preprocessing, feature engineering, and model training.
    Optimize AI frameworks to improve inference speed, reduce latency, and lower compute costs.
    Integrate AI models seamlessly into production software architectures and enterprise workflows.
  • Technical Skills Required
    AI/ML Frameworks & Libraries
    Core Frameworks: Deep expertise in PyTorch, TensorFlow, or JAX.
    GenAI Ecosystem: Hands-on experience with Hugging Face, LangChain, LlamaIndex, and vLLM.
    Core Languages: Mastery of Python for performance tuning.
  • Algorithms & Math
    Machine Learning: Deep understanding of regression, clustering, decision trees, and ensemble methods.
    Deep Learning: Strong grasp of Transformers, CNNs, RNNs, and reinforcement learning (RLHF).
    Data Infrastructure: Experience with Vector Databases (ChromaDB, Pinecone, Milvus) and SQL/NoSQL.
  • MLOps & Infrastructure
    Deployment: Experience with ML flow, Kubeflow, or Triton Inference Server.
    Cloud & Compute: Proficiency with AWS (SageMaker), Azure (Azure AI), or GCP (Vertex AI), alongside GPU acceleration (CUDA).
Experience & Qualifications
  • Experience: 8 years in data science or AI engineering, with 2+ years dedicated to Generative AI.
  • Education: Master's in Computer Science, Data Science, Mathematics, or a related quantitative field.

The base compensation range for this role in the posted location is: 79,000 to 105,000.

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include: 

  • Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
  • Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
  • Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
  • Life and disability insurance
  • Employee assistance programs
  • Other benefits as provided by local policy and eligibility

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.

Disclaimers

Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect.  We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.

Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.

Click the following link for more information on your rights as an Applicant in the United States.  http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.


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About United Airlines

Sourced by ZipRecruiter

United Airlines is embarking on an exciting journey to become the best airline in aviation history. Our purpose, "Connecting People, Uniting the World," extends beyond transportation, emphasizing our commitment to uplift and create opportunities in the places we serve. With a global presence and diverse workforce, we value inclusivity and are dedicated to hiring tens of thousands of individuals across various roles. Our comprehensive benefits package, including perks like space available travel, parental leave, and 401k, aims to support your well-being and growth.

Industry

Aviation

Company size

10,000+ Employees

Headquarters location

Chicago, IL, US

Year founded

1926

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