Machine Learning Engineer

Summary

Join VERSES, a cognitive computing company, as a Machine Learning Engineer on the Ecosystems team. You will contribute to sustainability, alignment, and multi-agent coordination projects, benchmarking active inference against reinforcement learning challenges. This 100% remote role (US or Canada) involves developing, deploying, and scaling multi-agent reinforcement learning models for sustainable urban solutions. Collaboration with research and product teams is key to transitioning research models into production systems. A Master's degree or higher in a related field and 2-3 years of relevant experience are required. VERSES offers a generous total rewards package, responsible paid time off, and a global virtual work environment.

Requirements

• Master’s degree or higher in Computer Science, Engineering, AI, or related fields
• A minimum of 2-3 years of experience developing and deploying machine learning models
• Proven proficiency in multi-agent reinforcement learning, decentralized AI, and smart city infrastructure modeling
• Strong software engineering skills, including experience with Python, ML deployment, and scalable systems

Responsibilities

• Develop, deploy, and scale multi-agent reinforcement learning models within decentralized AI systems for sustainable urban solutions (EcoNet)
• Integrate CityLearn framework and other smart city modeling tools to enhance adaptive, resilient urban infrastructure systems
• Collaborate closely with research and product teams to transition research models into production-ready systems
• Continuously refine system dynamics and decentralized AI system capabilities

Preferred Qualifications

• Familiarity with probabilistic programming, Active inference bonus
• Familiarity with CityLearn framework or similar AI-driven sustainability tools

Benefits

• Global virtual work environment (although some positions may need to operate within specific time zones)
• Responsible paid time off policy (RTO), and company-recognized Holidays
• Generous total rewards package