Staff ML Research Scientist (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Staff ML Research Scientist (AI): Architecting next-generation deep learning models for protein-ligand co-folding and affinity prediction with an accent on translating complex research into production-ready biopharma software. Focus on pioneering novel architectures, driving drug discovery pipelines, and championing scientific excellence within a high-impact research team.
Location: Must be based in Canada (Remote)
Salary: CA$180,000–CA$270,000
Company
is a high-growth AI company delivering Large Quantitative Models to solve critical challenges in life sciences, cybersecurity, and beyond.
What you will do
- Drive R&D for next-generation deep learning models for co-folding and affinity prediction.
- Bridge the gap between experimental research and commercial software utility.
- Optimize complex models for large-scale, real-world drug discovery applications.
- Represent scientific authority externally and shape internal technical strategy.
- Mentor junior researchers and foster a culture of technical rigor and rapid iteration.
Requirements
- PhD in Computer Science, Computational Chemistry, or related field.
- Must be based in Canada.
- At least 4 years of post-PhD professional industry experience.
- Proven track record in structure-based deep learned affinity modeling.
- Proficiency in Python and modern ML frameworks like PyTorch or JAX.
Nice to have
- Postdoctoral experience in deep learned structure-based affinity models.
- Experience shipping commercial-grade software in biopharma or tech.
- First-author publications in top-tier venues like NeurIPS, ICML, or CVPR.
- Familiarity with agentic coding tools.
Culture & Benefits
- Comprehensive medical, dental, and vision coverage with employer-paid premiums.
- Retirement savings plan with company matching.
- Flexible paid time off and company-wide seasonal breaks.
- Support for flexible work arrangements.
- Opportunities for professional growth, continuous learning, and cross-functional collaboration.
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