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Deep Learning Engineer Jobs in Quebec (NOW HIRING)

LawZero

Senior ML Research Scientist

Montreal, QC · On-site

Benchmark and optimize model performance and efficiency along with ML engineers to ensure the ... A track record of contributing to high-quality research projects in deep learning. What we offer

LawZero

Senior ML Research Developer

Montreal, QC · On-site

We are seeking a senior machine learning (ML) research developer to join our team working on a ... A track record of contributing to high-quality research projects in deep learning. What we offer

Maya HTT

AI Development Team Lead

Montreal, QC · Hybrid

Maya HTT is a world leading developer of digital industries software solutions. The world's top ... Strong background in deep learning (e.g., CNNs, transformers, detection/segmentation models)

Maya HTT

AI Development Team Lead

Montreal, QC · Hybrid

Maya HTT is a world leading developer of digital industries software solutions. The world's top ... Strong background in deep learning (e.g., CNNs, transformers, detection/segmentation models)

Exfo

Staff AI Developer / Architecte IA

Montreal, QC · On-site

Apercu des initiatives En tant que Staff AI Developer chez EXFO, vous aurez l'opportunite de ... Capacite a choisir entre deep learning et approches classiques. * Optimisation pour environnements ...

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Deep Learning Engineer information

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior Deep Learning Engineer or AI research director, often involving advanced skills in machine learning frameworks, data modeling, and large-scale system development. These roles usually require extensive experience, specialized knowledge, and may include leadership responsibilities or working in cutting-edge AI research environments.

What is a Deep Learning Engineer job?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What are the key skills and qualifications needed to thrive in the Deep Learning Engineer position, and why are they important?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

What engineers make $500,000?

Senior engineers in high-demand fields such as software, data science, and machine learning can earn $500,000 or more annually, especially with extensive experience, specialized skills, and leadership roles. Roles like senior software engineers, machine learning engineers, and data architects at large tech companies or startups often reach this compensation level through base salary, bonuses, and stock options.

What do deep learning engineers do?

Deep learning engineers develop and implement neural network models to solve complex problems such as image recognition, natural language processing, and speech analysis. They work with large datasets, use frameworks like TensorFlow or PyTorch, and often require knowledge of programming, mathematics, and machine learning principles.

What are the typical daily tasks and responsibilities of a Deep Learning Engineer?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What engineers make $300,000 a year?

Senior deep learning engineers and AI specialists with extensive experience, advanced skills in machine learning frameworks, and strong domain knowledge can earn $300,000 or more annually. These roles often require advanced degrees, certifications, and work in high-demand industries such as technology, finance, or healthcare, typically involving leadership responsibilities and complex project management.
What are popular job titles related to Deep Learning Engineer jobs in Quebec? For Deep Learning Engineer jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Deep Learning Engineer jobs in Quebec look for? The top searched job categories for Deep Learning Engineer jobs in Quebec are:
Deep Learning Engineer jobs near you

Research Scientist, Next-Generation Structural Biology & Atomistic Modeling

Valence Labs

Montreal, QC • Hybrid

Other

Posted 11 days ago

Job description

About Valence Labs

Valence Labs is Recursion's frontier AI research engine. We lead high-impact research programs designed to materially expand Recursion's ability to discover and develop medicines for complex diseases.

Our team balances near-term pragmatism with a long-term view of where the field is heading in the next 3-5 years, incubating, designing, and productizing the approaches we believe will define the future of drug discovery. Our work is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, contribute to open science, and engage with some of the world's most active ML-for-drug-discovery research communities. Our teams are based in London and Montreal, with deep ties to Mila, the world's largest deep-learning research institute.

About The Role

We are seeking a Research Scientist with a hybrid research-engineering mindset to join our team. In this role, you will be at the forefront of developing generative architectures and foundation models that ground machine learning in real-world physical and biological discovery. You will focus on accelerating and improving the accuracy of molecular design and structural biology workflows-specifically targeting the intersection of physics-informed frameworks and data-driven ML to solve complex protein-ligand interaction challenges.

Key Responsibilities
  • Model Innovation: Research and develop state-of-the-art architectures (e.g., flow matching, diffusion models, geometric deep learning) tailored to modeling protein-ligand interactions.
  • Physics-ML Integration: Develop hybrid approaches that integrate co-folding, molecular dynamics (MD), and experimental potency data to achieve high-resolution accuracy on novel targets.
  • Scalable Engineering: Build and maintain ML systems capable of processing massive datasets, such as protein-ligand simulations, on high-performance compute clusters (BioHive).
  • Biological Grounding: Ensure ML predictions are biologically trustworthy and actionable by collaborating closely with drug discovery teams to reduce cycle periods and dead ends in lead optimization.
  • Open Science & Collaboration: Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, JACS) and contribute to the broader scientific community.
A successful candidate will have most of the following:
  • PhD (or equivalent) with significant academic or industry research experience in machine learning applied to structural biology, atomistic modeling, or physical simulation.
  • Scientific knowledge of physics and chemistry, with a deep understanding of physical constraints and invariances in molecular systems.
  • Impactful research track record, including experience with equivariant models, generative modeling of molecular systems, or replacing traditional physics workflows (like ABFE) with ML-driven alternatives.
  • Strong technical and engineering skills, including proficiency in Python and the ability to build scalable, reproducible experiment pipelines.
  • Interdisciplinary empathy, with a proven ability to work effectively with medicinal chemists and biophysicists to ensure models solve real-world drug discovery problems.
  • Leadership and communication skills, including the ability to explain complex ideas clearly to both technical and non-technical stakeholders.
Working Location & Compensation:

This is an office-based, hybrid position at either of our offices located in Montreal, Quebec, Canada. Employees are expected to work in the office at least 50% of the time.

Compensation packages are competitive and commensurate with the skills and level of experience required for this role. In addition to base salary you will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. 

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