Machine Learning Scientist

4 weeks ago


Toronto Montreal Calgary Vancouver Edmonton Old Toronto Ottawa Mississauga Quebec Winnipeg Halifax Saskatoon Burnaby Hamilton Victoria Surrey Halton Hills London Regina Markham Brampton Vaughan Kelowna Laval Southwestern Ontario R, Canada Synlico Inc. Full time

Get AI-powered advice on this job and more exclusive features. Synlico Inc. is a resident company of Johnson & Johnson Innovation – JLABS, a premier life science incubator program. Synlico envisions rewriting medicine by bringing causality to cellular biology. We are an AI-powered Drug Discovery startup developing cutting‑edge AI platform to combat diseases with the latest advancements in single‑cell bioinformatics, machine learning, and causal discovery. We are a passionate team of young scientists and professionals who value innovation and teamwork. Our research is highly interdisciplinary and team oriented. We are seeking a talented and enthusiastic machine learning scientist who will lead/participate in several of our projects with our science team. Our HQ is at South San Francisco, CA, United States. For more information, please visit www.synlico.com. Job Description This is a remote working position in Canada. Invent & deliver: Design, prototype, and productionize generative models for cellular‑level biological phenomena. Bridge research to product: translate promising prototypes into robust and well‑tested systems. Collaborate cross‑functionally: Foster strong collaboration with bioinformatics, applied ML, and causal teams. Lead innovation initiatives: Leading innovation initiatives and incorporating fresh ideas and technologies. Raise the bar: Mentor scientists on experimental design, coding standards, and reproducibility. Job Requirements Must have a Ph.D in Computer Science, Artificial Intelligence or related field. 3–5+ post‑Ph.D years of industry experience building and deploying ML models. Demonstrated research excellence (e.g., strong publication record in top‑tier AI venues) and ability to convert insights into working systems. Depth in generative modeling, especially generative flow models and probabilistic generative methods. Knowledge on causal structure discovery and causal inference is a plus but not required. Strong coding and problem‑solving skills. Excellent communication and collaboration skills, proven ability to work independently and collaboratively with minimal supervision. Experience defining research roadmaps, mentoring peers, and contributing to IP/open‑source. Passionate about addressing complex, real‑world challenges and creating impactful, innovative solutions. Interested candidates please submit your CV with a full list of your publications. #J-18808-Ljbffr



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