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Phd Machine Learning Jobs in Ontario (NOW HIRING)

What you will do ACV's Machine Learning organization is looking for a talented Machine Learning ... Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience ...

Senior Machine Learning Engineer

Toronto, ON ยท Remote

$165K - $225K/yr

... PhD (preferred) or Master's degree in Computer Science, Electrical Engineering, or a related field Deep expertise in computer vision and deep learning, with hands-on experience in one or more of:

Senior Machine Learning Engineer

Toronto, ON ยท Remote

$165K - $225K/yr

... PhD (preferred) or Master's degree in Computer Science, Electrical Engineering, or a related field Deep expertise in computer vision and deep learning, with hands-on experience in one or more of:

CA$100K - CA$500K/yr

Hands-on experience training large-scale machine learning models. * 4+ years of industry and/or ... PhD, published research, or experience with speculative decoding is highly valued. What We Need

ACV's Machine Learning organization is looking for a talented Machine Learning Engineer III to join ... Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience ...

ACV's Machine Learning organization is looking for a talented Machine Learning Engineer IV to join ... Graduate education (MS or PhD) in a computationally intensive domain or equivalent work experience ...

Qualifications: - Pursuing PhD degree in Computer Science, Engineering, AI, Machine Learning, Computer Vision, Robotics and/or similar technical field(s) of study. - Demonstrated research/software ...

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Showing results 1-20

Phd Machine Learning information

See Ontario salary details

$22K

$119K

$214.5K

How much do phd machine learning jobs pay per year?

As of Jul 19, 2026, the average yearly pay for phd machine learning in Ontario 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, and why are they important?

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.

Infographic showing various Phd Machine Learning job openings in Ontario as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $119,007 per year, or $57.2 per hour.

Senior Research Scientist, Machine Learning (BioFM)

Deep Genomics

Toronto, ON โ€ข On-site

$175 - $200/hr

Other

Medical, Dental, Vision, Life, PTO

Posted 5 hours ago


Job description

About Us

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created.

Opportunity

We are seeking an exceptional and creative Senior/Staff Machine Learning Scientist to lead and innovate within our core AI research team, specifically focusing on the creative building of Biological Foundation Models (BioFMs). You will pioneer novel deep learning architectures and pre-training paradigms that learn the fundamental language of the genome and cellular biology. Rather than just applying out-of-the-box ML to biological datasets, you will design the next generation of BioFMs from tackling complex -omics data at scale. If you are a first-principles thinker excited to bridge advanced ML with genome biology to solve high-impact, frontier problems in human health and drug discovery, this is a unique opportunity.

Key Responsibilities
  • Lead the creative research, architecture design, and training of Biological Foundation Models (BioFMs), on massive-scale genomic, transcriptomic, and single-cell datasets.
  • Collaborate closely with computational biologists and drug developers to integrate deep biological priors directly into model architectures and training objectives, ensuring our BioFMs capture fundamental and scientifically meaningful representations.
  • Rigorously implement, train, debug, and evaluate large-scale models to demonstrate scientific validity and drive progress on frontier problems in human health and genetic medicines.
  • Stay current with advancements in machine learning and computational biology research, identifying cross-disciplinary applications to solve real-world challenges.
  • Mentor junior scientists and engineers, fostering a culture of technical excellence and scientific curiosity through leadership and high-quality code review.
  • Share research findings through internal presentations and contribute to the scientific community via publications in top-tier venues.
Basic Qualifications
  • PhD (or evidence of equivalent level of expertise) with a strongly distinguished research focus in Computational Biology, Machine Learning, Computer Science, or a related quantitative field.
  • Deep understanding of modern deep learning and the creative building of foundation models, including CNNs, Transformers, and related sequence models (e.g., state-space models) specifically tailored for biological or genomic sequence data.
  • A demonstrated track record of building and scaling AI models for complex biological datasets (e.g., single-cell genomics, DNA/RNA sequences) from initial conception to production.
  • Proven ability to implement, train, and debug highly-performant deep learning models using frameworks like PyTorch.
  • Experience working with massive datasets and a deep understanding of the engineering and algorithmic challenges associated with scale.
  • Excellent communication skills, capable of discussing complex ideas seamlessly with both ML engineers and biological domain experts.
Preferred Qualifications
  • A strong track record of impactful research demonstrated through first-author publications in high-impact scientific journals (e.g., Nature, Science, Cell) or top-tier ML/CompBio conferences (e.g., NeurIPS, ICML, ICLR, ISMB, RECOMB).
  • 2+ years of relevant post-graduate experience at a leading industrial R&D lab or in a highly competitive academic environment building genomics AI.
  • Experience technically leading projects or mentoring junior researchers/engineers.
  • Proficiency with cloud computing platforms (e.g., GCP) for large-scale model training and experimentation.
  • Contributions to open-source projects demonstrating the ability to solve complex research problems in ML or computational biology.
What We Offer
  • A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery.
  • Highly competitive compensation, including meaningful stock ownership.
  • Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program.
  • Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
  • Maternity and parental leave top-up coverage, as well as new parent paid time off.
  • Focus on learning and growth for all employees - learning and development budget & lunch and learns.
  • Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.

Deep Genomics encourages applications from all backgrounds who seek the opportunity to build the world's leading AI-driven genetic medicine company.

If you have a disability or special need, accommodation is available on request for candidates taking part in all aspects of the selection process.

*This posting reflects a current vacancy.

We offer competitive compensation aligned with local market benchmarks. The salary range for this role is $175,000 - $200,000, and reflects Canada-based roles; compensation may differ for U.S.-based candidates.

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