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Phd Machine Learning Jobs in Quebec (NOW HIRING)

We are seeking a Senior Machine Learning (ML) Research Scientist to join our team working on a ... A PhD is preferred but not required if the candidate demonstrates exceptional abilities. * 5+ years ...

Develop, validate and deploy statistical, machine learning and generative AI solutions, including ... PhD highly regarded). * Extensive experience delivering end-to-end AI and data science solutions ...

Develop, validate and deploy statistical, machine learning and generative AI solutions, including ... PhD highly regarded). * Extensive experience delivering end-to-end AI and data science solutions ...

New

Currently pursuing a Bachelor's, Master's, or PhD degree in a field related to artificial ... At least one previous internship in data science, artificial intelligence, machine learning, or ...

Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI ... Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related ...

... machinery and process control. In the R&D team we focus on understanding polymer behavior ... Masters or PhD in Material Science, Polymers Science or Mechanical Engineering * Strong intuition ...

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Phd Machine Learning information

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$22K

$119K

$214.5K

How much do phd machine learning jobs pay per year?

As of Sep 15, 2026, the average yearly pay for phd machine learning in Quebec is $119,007.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,500.00 and $159,000.00 per year, depending on experience, location, and employer.

What is a PhD in machine learning?

A PhD in Machine Learning is an advanced doctoral degree focused on developing new algorithms, theories, and applications in the field of machine learning. Graduates typically conduct original research, contribute to academic publications, and often specialize in areas like deep learning, reinforcement learning, or probabilistic modeling. This degree prepares individuals for careers in academia, industry research labs, or leadership roles in tech companies. The program usually involves coursework, comprehensive exams, and the completion of a dissertation based on novel research.

What are the key skills and qualifications needed to thrive as a PhD-level machine learning professional?

To thrive as a PhD-level Machine Learning professional, you need deep expertise in mathematics, statistics, computer science, and advanced machine learning algorithms, typically supported by a doctoral degree. Proficiency with programming languages like Python or R, machine learning frameworks such as TensorFlow or PyTorch, and experience with large-scale data systems are essential. Strong problem-solving skills, critical thinking, and effective communication set outstanding candidates apart by enabling them to tackle complex research challenges and collaborate across teams. These skills and qualities are crucial for driving innovation, publishing research, and developing impactful machine learning solutions.

What are some common challenges faced by PhD-level professionals in machine learning when transitioning from academia to industry roles?

PhD graduates in machine learning often encounter challenges such as adapting to faster-paced project timelines, aligning research with business objectives, and collaborating in multidisciplinary teams. Unlike academia, where projects can be exploratory and long-term, industry roles usually require actionable results within shorter deadlines. Additionally, communicating complex technical ideas to non-technical stakeholders and prioritizing practical solutions over theoretical novelty are key adjustments. However, these challenges also present opportunities for professional growth and broader impact.

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

AspectPhd Machine LearningData Scientist
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch labs, academia, R&D departmentsBusiness, tech companies, analytics teams
Industry UsageResearch-focused roles, advanced algorithm developmentData analysis, model building, business insights
Common Search/ComparisonYesYes

While both roles involve working with data and algorithms, a Phd Machine Learning typically focuses on research, developing new models, and theoretical work, often in academic or R&D settings. A Data Scientist applies these techniques to solve practical business problems, analyze data, and generate insights in industry environments.

How much does a PhD in machine learning make?

A PhD in machine learning typically earns between $100,000 and $150,000 annually in industry roles, with salaries increasing for senior positions or in high-demand sectors. Academic positions may offer lower salaries but include research funding and teaching responsibilities.

What can you do with a PhD in machine learning?

A PhD in machine learning prepares individuals for advanced roles such as research scientist, machine learning engineer, data scientist, or AI specialist. These roles involve developing algorithms, analyzing large datasets, and applying AI techniques across industries like technology, healthcare, finance, and autonomous systems. Strong programming skills and knowledge of tools like Python, TensorFlow, or PyTorch are essential for these positions.
Infographic showing various Phd Machine Learning job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $119,007 per year, or $57.2 per hour.

Mathematical Scientist for AI Safety Research

Montreal, QC

Full-time

Medical, Retirement, PTO

Re-posted 26 days ago


Key responsibilities

  • Explore new AI system architectures, theoretical frameworks, and safety mechanisms to develop a blueprint for safe AGI with safety guarantees.

  • Analyze, refine, and validate formal requirements for the Scientist AI, including proving probabilistic safety guarantees under finite data and compute constraints.

  • Contribute to and evaluate existing theoretical research, and disseminate findings through scientific papers.


Job description

Frontier AI companies are throwing billions of dollars into scaling existing architectures and methods such as next-token-prediction, direct preference optimization (DPO), reinforcement learning with human feedback (RLHF), and reinforcement learning with verified rewards (RLVR). These methods are very powerful, yet fundamentally flawed, resulting in misalignment, sycophancy, systematic biases, and other forms of harmful behavior that are already having severely negative consequences in our society.

LawZero is a non-profit founded by Yoshua Bengio developing a fundamentally new approach, the Scientist AI. We are inspired by the fact that scientific theories are both generally useful and, unlike an untrusted agent, equivariant to the consequences of their use (for a broad overview, see our blogpost). We aim not only to build a novel, safe-by-design system, but to construct a theoretical blueprint for safe AI systems, which are at once capable and come with probabilistic safety guarantees, under finite data and compute constraints.

We are seeking an outstanding theoretical researcher to contribute to the foundational and theoretical efforts here working with Yoshua Bengio and our world-class team of researchers and engineers.

Key responsibilities

  • Boldly go where no one has gone before in exploring new AI system architectures, theoretical frameworks, and new safety mechanisms in search of a  blueprint for safe AGI and corresponding safety guarantees..
  • Work closely with Yoshua Bengio and other leading researchers to analyze and refine the formal requirements needed to safely build and deploy the Scientist AI, including proving probabilistic safety guarantees under finite data and compute constraints; and, as the system matures, helping our research teams with empirical validation.
  • Analyze, evaluate and contribute to the existing theoretical work already undertaken at LawZero by Yoshua Bengio and leading researchers, including the writing and dissemination of scientific papers.
  • Read vigorously to stay up-to-date with relevant AI safety and machine learning research and deeply understand the risks posed by frontier models, including misalignment, reward hacking, instrumental goals, etc.
  • Scrutinize and contribute to LawZero's broader research agenda in order to guide and prioritize experiments to answer critical safety questions efficiently, in a way that does not prevent the highest level of capability.
  • Share your knowledge about AI Safety and help researchers and engineers at LawZero better design and implement new ML algorithms and experiments.

Skills and qualifications

  • A PhD  in machine learning, computer science, mathematics, statistics, or an adjacent area relevant to the Scientist AI research program.
  • A track record of research, especially in the formalization and fruitful technical analysis of subtle natural-language intuition.
  • Strong interest and ideally experience with technical AI safety, including an exceptional ability for abstract systems thinking.
  • A deep understanding of the mathematical underpinnings commonly used in the design and analysis of probabilistic machine learning, especially for neural networks and language models. 
  • The perseverance to work through hard and ill-defined problems, and to use confusion and dead-ends as challenges to dig deeper and further understanding.
  • Clear communications: the ability to articulate complex ideas verbally and in writing to both technical and non-technical audiences.
  • The ability to work in a collaborative environment and contribute to collective research goals.

Nice to have:

  • The ability to regularly travel alongside Yoshua and team members to conferences is a big plus, as it will significantly accelerate the work!

What we offer

  • The chance to contribute meaningfully to a globally critical initiative
  • Comprehensive health benefits (including mental health and wellness management account)
  • 20 days of vacation per year upon start
  • Employer contribution of 4% to your retirement savings, with no required employee match
  • Additional compensation totaling 8% of your salary to apply towards additional retirement savings or bonuses (independent of group and individual performance)
  • A team of passionate world-class experts in their field
  • A collaborative and inclusive work environment in our vibrant office space in the heart of Little Italy, in the trendy Mile-Ex district, close to public transportation