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Algorithm Research Jobs in Toronto, ON (NOW HIRING)

Research Engineer, Calibration

Toronto, ON · On-site +1

CA$158K - CA$269K/yr

To learn more visit: www.waabi.ai As a Research Engineer in Calibration, you will create the next ... You should understand how to leverage classical algorithms (e.g. ICP, RQE, SLAM, visual and radar ...

The Foundry Research and Development team is built on a foundation of research and development ... Guides the refinement and optimization of models and algorithms through feature engineering ...

This is not a greenfield research project. The algorithms exist, they run, and they produce results for real customers. Your job is to understand the existing system deeply, identify where ...

You will create large scale data processing pipelines to help researchers build and train novel machine learning algorithms.You willdevelophigh performing scalablesystems in the context of large ...

... Research and implement the state-of-the-art computer vision and Vision Language models algorithms. - Collaborate with product managers and engineering teams to design and implement computer vision ...

... Research and implement the state-of-the-art computer vision and Vision Language models algorithms. - Collaborate with product managers and engineering teams to design and implement computer vision ...

... to bring research ideas into production and push the boundaries of self-driving technology. You will... - Prototype, evaluate, and iterate on perception algorithms, using real-world data and ...

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Algorithm Research information

Is ML a high paying job?

Machine Learning (ML) roles, including those in algorithm research, are generally well-paid due to the high demand for specialized skills in data analysis, programming, and statistical modeling. Salaries vary based on experience, location, and industry, but advanced ML positions often offer competitive compensation compared to other tech roles.

What is algorithmic research?

Algorithm research involves studying and developing new algorithms to solve computational problems efficiently. It requires understanding theoretical concepts, analyzing algorithm performance, and often involves programming and testing in environments like Python or C++. This work supports advancements in fields such as artificial intelligence, data analysis, and software development.

What are the key skills and qualifications needed to thrive as an Algorithm Researcher, and why are they important?

To excel as an Algorithm Researcher, you need a strong background in mathematics, computer science, and algorithm design, often supported by an advanced degree such as a master's or PhD. Proficiency with programming languages (like Python, C++, or Java), machine learning frameworks, and version control systems is essential. Analytical thinking, creativity, and effective communication are crucial soft skills that set top performers apart in this field. These skills are vital for developing innovative, efficient solutions and collaborating within interdisciplinary teams to solve complex computational problems.

Which 3 jobs will survive AI?

Algorithm research jobs are likely to persist because they involve developing new algorithms and understanding complex data, tasks that require human creativity and critical thinking. Roles in healthcare, such as medical professionals, and skilled trades like electricians or plumbers, are also expected to remain in demand due to the need for hands-on expertise and human judgment. These jobs often require specialized knowledge, certifications, or physical skills that are difficult for AI to replicate fully.

Is AI replacing algorithms?

Algorithm research involves developing and improving algorithms, which are fundamental to AI systems. AI often relies on algorithms to process data and make decisions, but it does not replace the need for algorithm development; instead, AI advances can lead to new algorithmic techniques and improvements. Researchers in this field focus on creating efficient, effective algorithms that support AI applications and other computational tasks.

What is Algorithm Research?

Algorithm research involves studying, designing, analyzing, and optimizing algorithms to solve complex problems efficiently. Researchers in this field explore new computational methods, improve existing algorithms, and evaluate their performance in various contexts. This work is fundamental in areas like computer science, artificial intelligence, data science, and cryptography, driving technological advances and innovation.

What are the typical challenges faced by professionals in Algorithm Research roles and how can they best address them?

Algorithm Research professionals often encounter challenges such as bridging the gap between theoretical solutions and practical implementation, staying updated with rapid advancements in the field, and collaborating with cross-functional teams to integrate research outcomes into real-world products. To address these challenges, it is helpful to maintain strong communication with engineering teams, participate in continual learning through academic papers and conferences, and adopt an iterative approach to testing and refining algorithms. Building a habit of documenting experiments and results also streamlines collaboration and future development.

What is the difference between Algorithm Research vs Data Scientist?

AspectAlgorithm ResearchData Scientist
Required CredentialsAdvanced degrees in CS, Mathematics, or related fieldsDegree in CS, Statistics, or related fields; certifications like SAS or Python
Work EnvironmentResearch labs, R&D departments, academiaBusiness environments, analytics teams, tech companies
Industry UsageDeveloping new algorithms, theoretical researchAnalyzing data, building predictive models, insights generation
Common Search/ComparisonYesNo

Algorithm Research focuses on developing and testing new algorithms, often in research or academic settings, requiring advanced technical credentials. Data Scientists analyze data to generate insights and build models, working primarily in business environments. While both roles involve data and programming, their core objectives and work settings differ significantly.

Infographic showing various Algorithm Research job openings in Toronto, ON as of July 2026, with employment types broken down into 4% Locum Tenens, 63% Full Time, 23% Part Time, 3% Contract, 6% Nights, and 1% Summer. Highlights an 76% Physical, 2% Hybrid, and 22% Remote job distribution.

Senior Research Scientist, Machine Learning (BioFM)

Deep Genomics

Toronto, ON • On-site

$175 - $200/hr

Other

Medical, Dental, Vision, Life, PTO

Posted 23 days 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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