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Senior Machine Learning Engineer Jobs in Laval, QC

Apertera

Generative AI Engineer

Montreal, QC · On-site

Work closely with machine learning engineers and data engineers to design, build, and test models. * Develop efficient and scalable algorithms for training and inference of generative models ...

Td

Sr. Full Stack Data Science Engineer

Montreal, QC · On-site

CA$154K - CA$199K/yr

Solid knowledge of applied Machine Learning, Deep Learning, Large Language Models * Solid cloud ... Data Engineering : ETL/ELT Pipelines, Apache Spark Nice-to-Have * Experience in customer analytics ...

Valtech

Scientifique des donnees senior

Montreal, QC · On-site

Role Nous recherchons un(e) Data Scientist Senior pour rejoindre une equipe hautement collaborative ... Ce poste se situe a l'intersection de la Data Science, du Machine Learning et de l'ingenierie, avec ...

Intact Financial

Senior Data Scientist

Montreal, QC · On-site

... analytics, engineering, product) on high-impact end-to-end use cases (anomaly detection ... Use machine learning and advanced statistical methods to identify trends and patterns in complex ...

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All JobsMachine Learning Engineer JobsSenior Machine Learning Engineer JobsSenior Machine Learning Engineer Jobs in Laval, QC

Senior Machine Learning Engineer information

See Laval, QC salary details

$44.9K

$164.8K

$247.8K

How much do senior machine learning engineer jobs pay per year?

As of Sep 3, 2026, the average yearly pay for senior machine learning engineer in Laval, QC is $164,836.00, according to ZipRecruiter salary data. Most workers in this role earn between $137,096.00 and $183,460.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

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

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

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

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What cities near Laval, QC are hiring for Senior Machine Learning Engineer jobs?

Cities near Laval, QC with the most Senior Machine Learning Engineer job openings:

Senior Machine Learning Engineer jobs near you
Infographic showing various Senior Machine Learning Engineer job openings in Laval, QC as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $164,836 per year, or $79.2 per hour.

Generative AI Engineer

Apertera

Montreal, QC • On-site

Full-time

Re-posted 10 days ago

Job description

Generative AI EngineerAbout Apertera

Apertera is leading the evolution of language solutions for high-stakes content. We partner with enterprises as an extension of their teams, combining professional expertise with Adaptive AI technology that is continuously refined by client context.

For more than twenty years, Apertera has set the bar for legal, financial, and regulatory translation, serving the most rigorous buyers, including over 75% of major national Canadian law firms, all major banks, and leading securities regulators.
Apertera is Canadian-owned, ISO 17100 and SOC 2 certified.

Our core values: 

  • Innovation
  • Dedication
  • Fanatical commitment to quality and service
  • Resourcefulness
  • Collaboration
About the Role

We are looking for a Generative AI Engineer to develop our next-generation intelligent translation and translation-related service engine, using Generative AI (GenAI) and Large Language Model (LLM) technologies. You will report to the team lead in AI Innovation, develop and implement state-of-the-art algorithms by fast prototyping, and collaborate with the software team to deploy models. We expect our Generative AI Engineer to to work at the intersection of LLM engineering, machine translation, cloud infrastructure, and evaluation. You'll play a pivotal role in pushing the boundaries of applying GenAI to translation scenarios and create innovative solutions.

Responsibilities
  • Implement state-of-the-art LLM techniques including continued pre-training, instruction fine-tuning, preference alignment, and LLM deployment.
  • Work closely with machine learning engineers and data engineers to design, build, and test models.
  • Develop efficient and scalable algorithms for training and inference of generative models, leveraging deep learning frameworks such as TensorFlow or PyTorch and optimizing performance on diverse hardware platforms.
  • Train and evaluate generative models using appropriate metrics and benchmarks, fine-tuning model parameters, architectures, and hyperparameters to optimize performance, stability, and generalization.
  • Built end-to-end prototypes that are production ready.
  • Work closely with software and DevOps engineers to deploy GenAI models.    
  • Document code, algorithms, and experimental results, following best practices for reproducibility, version control, and software engineering, and contributing to internal knowledge sharing and continuous improvement initiatives.
Requirements
  • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields. A Master’s degree is preferred.
  • 2+ years of industry experience developing GenAI and LLM applications is preferred.
  • Proficiency in Python programming and software development practices, with experience in building and maintaining scalable, production-grade software systems.
  • Working knowledge and project-based record of all of the following: context engineering, RAG, harness engineering.
  • Working knowledge and project-based record of at least one of the following is a plus: LLM post-training, APO, agentic workflow.
  • Strong problem-solving skills, attention to detail, and the ability to work independently and collaboratively in a fast-paced environment.
  • Hands-on experience with Huggingface APIs or Amazon Bedrock. 
  • Expert skills of Python, including PyTorch, TensorFlow, Pandas, etc.
  • Experience with cloud platforms like AWS, GCP, or Azure 
  • Excellent problem-solving skills, critical thinking, and the ability to work independently and collaboratively in a fast-paced environment.
  • Strong communication skills, with the ability to articulate complex technical concepts effectively and work cross-functionally with diverse teams.
  • Self-driven, self-motivated with excellent time management skills
  • Excellent organizational, communication, and interpersonal skills
  • Ability to adapt to shifting priorities without compromising deadlines and momentum.

 

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