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Détails du poste

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 part time working position in Canada. As a member of our science team, the Reinforcement Learning Scientist will Design state-of-the-art generative model for biological systems at the cellular level. Foster strong collaboration with bioinformatics, ML, and causal teams and provide expert opinions to project peers. Leading innovation initiatives and incorporating fresh ideas and technologies. Job Requirement: Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field. Strong research record, including publications at leading peer-reviewed AI venues. Deep understanding of RL fundamentals, including MDPs, Bellman operators, temporal-difference learning, policy gradients, actor-critic methods, and on-policy versus off-policy learning. Strong practical expertise in value-based deep RL for discrete or structured action spaces, including replay-based learning, target networks, multi-step targets, and modern stabilization techniques. Demonstrated hands-on experience applying maximum-entropy (Soft) RL to real-world or research-grade problems, including implementing and solving non-trivial environments using algorithms such as Soft Q-Learning or discrete Soft Actor-Critic. Experience building novel environments from scratch and working with large, variable, constrained, or combinatorial action spaces. Strong understanding of exploration, sparse or delayed rewards, long-horizon credit assignment, and common sources of deep-RL instability. Current knowledge of modern RL research, along with excellent communication, collaboration, and independent problem-solving skills. Interested candidates please submit your CV with a full list of your publications. Synlico is an equal opportunity employer. At Synlico, we value differences and are committed to a diverse workplace that fosters inclusion for all employees. Synlico provides a work environment that respects each individual and is free of all forms of employment discrimination because of race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or veteran status.

Ce que vous ferez

The scientist will design state-of-the-art generative models for biological systems at the cellular level. They will collaborate with bioinformatics and causal teams to lead innovation initiatives and incorporate new technologies.

Exigences

Requires a Ph.D. in Computer Science, AI, or Machine Learning with a strong research record and publications in peer-reviewed venues. Candidates must have deep theoretical and practical expertise in Reinforcement Learning, specifically value-based and maximum-entropy RL.

Compétences indiquées

• Apprentissage automatique · Souhaitée
• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Reinforcement Learning
• Generative Models
• Causal Discovery
• Deep RL
• Soft Actor-Critic
• MDPs
• Policy Gradients
• Bioinformatics
• Single-cell Biology
• Python
• Machine Learning
• Actor-Critic Methods
• Value-based RL
• Temporal-difference Learning
• Bellman Operators
• Combinatorial Action Spaces

Domaines d’emploi

• Science & Research
• Technology
• Healthcare
• Data & Analytics
• Software