Machine Learning Scientist - Reinforcement Learning

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.

The Reinforcement Learning Scientist will design state-of-the-art generative models for biological systems at the cellular level and foster collaboration with bioinformatics, ML, and causal teams. They will also lead innovation initiatives and incorporate fresh ideas and technologies.

Candidates must have a Ph.D. in a relevant field and a strong research record, including publications in leading AI venues. They should possess a deep understanding of reinforcement learning fundamentals and practical expertise in value-based deep RL.

Identified from the job description. Confirm important requirements above.

• Reinforcement Learning
• Machine Learning
• Causal Discovery
• Bioinformatics
• Generative Models
• Deep Learning
• Policy Gradients
• Actor-Critic Methods
• Exploration Strategies
• Sparse Rewards
• Temporal-Difference Learning
• Maximum-Entropy RL
• Soft Q-Learning
• Discrete Soft Actor-Critic
• Collaboration
• Problem-Solving

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

Minimum education

Master’s degree

Minimum experience

5+ years

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level

Application method

Direct apply is available