Machine Learning Scientist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 4h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 192d ago
The date the source published, not the day we noticed it (2026-03-25). Last seen at its source 2h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Canada
The description states no restriction of its own. This is the source's own tag.
Pay
$150k–200k/yr
Published by the source in its own salary field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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bamboohr employer's own board first seen 4h ago · last seen 2h ago
The listing
VANCOUVER, BC (OR REMOTE*) / 2+ YRS PROFESSIONAL EXPERIENCE
Small molecule drug discovery is one of the most exciting open problems in machine learning. Traditional approaches require over ten years and two billion dollars to develop a new pharmaceutical, and their reliance on trial-and-error calls out for better predictive and generative models. The existent datasets are large enough to benefit from sophisticated deep learning architectures, but small enough that ML models can be trained in a few days, facilitating rapid experimentation and innovation. Nevertheless, the current industry standard has progressed little beyond shallow ML techniques such as random forests and support vector machines, largely due to the difficulty of integrating world-class machine learning research with chemistry and pharmacology expertise.
Variational AI is searching for a machine learning scientist to join us in our quest to radically accelerate the development of new drugs through machine learning excellence. For over six years, we have been advancing the state-of-the-art, and delivering projects to customers including Merck, Rakovina Therapeutics, and ImmVue Therapeutics.
You will help design, implement, test, and refine novel elements of a machine learning architecture built from the ground up to optimize the properties of small molecule drugs; continually improve the robustness of our existing code base; and apply our pipeline to new drug targets. Experience developing novel ML algorithms in domains such as diffusion models, Transformers, graph neural networks, uncertainty quantification, and Bayesian optimization is required, but we can provide all necessary background in chemistry, pharmacology, and biology.
Here is the background we’re looking for:
- Ph.D. in CS, applied mathematics, statistics, physics, or related discipline;
- Expertise with machine learning techniques, including diffusion models, Transformers, and Bayesian optimization, demonstrated through first-author publications in conferences like NeurIPS, ICLR, and ICML;
- Two or more years’ experience developing robust code on larger projects, including code review, refactoring, unit testing, version control, etc.;
- Mastery of Python and PyTorch; and
- Intellectual curiosity and drive to excel.
We are an equal opportunity employer and enthusiastically welcome applications from women, BIPOC, and members of underrepresented communities and groups. Compensation is a competitive mix of cash and options. We prioritize expertise and passion over where you decide to live and work; however, for collaboration across our team, applicants must be based in *North American time zones.
To learn more about us, you can find some of our recent work at variationalai.substack.com