Are you looking for a career-defining challenge working on building an autonomous system? Are you wishing to contribute to a green and sustainable future in living spaces?

ABOUT PASSIVELOGIC

PassiveLogic is the first fully autonomous platform for buildings. We’ve reinvented the fundamental principles of automation to democratize technology, optimize buildings, and reduce the world’s carbon footprint. We are a team of technologists, engineers, and creatives dedicated to making a sustainable impact through real-world solutions.
We are looking for team members who have a passion for technology and want to work on cutting-edge problems with real-world solutions. Our culture is built on bringing together the most talented engineers, thinkers, and creatives—backed by the world’s leading investors—working together to make the future a reality.

WHAT YOU’LL BRING

If your experience does not meet all our posted requirements below, we’d still love to hear from you. We are looking for practitioners who are passionate about understanding people, committed to lifelong learning, and driven by the love of what they do. If that’s you, please apply!

ABOUT THE ROLE

This is a career-defining opportunity to play a crucial role in a hyper-scale AI company that is transforming the future of autonomous systems, energy, and the built environment.
As an Autonomous Systems Pathfinding Engineer, you will play a foundational role in our AI and autonomous services to improve building autonomous control performance and occupant comfort. You will identify the workflows a system should follow for making autonomous decisions and create tools and learning mechanisms to enable the autonomy of these systems.

WHAT YOU’LL DO

Control Pathfinding Agent: Develop and maintain the infrastructure to perform real-time decision making at the edge, using existential ontological reasoning and differentiable physics simulation inferencers. Implement the control pathfinder agent in PassiveLogic’s Hive controller infrastructure and integrated software tools.
In-suite Autonomous Learning: Enhance the adaptability of the system over time. This involves developing an agent for autonomous sensor data fusion, quantifying the quality of empirical/prediction data, continuous physics model learning and state estimation for improving prediction accuracy.
Collaborate Across Teams: Work closely with multidisciplinary teams, including Digital Twins, AI Frameworks, Formal Methods, and the platform team, for system integration validation and proving levels of autonomy.
Design Interfaces: Develop an interface with the object model through Object Relational Mapping.
Creating Robust Solutions: Create building systems control route optimization and real-time decision-making using technologies like machine learning, graph theory, and sensor fusion.
Monitoring and Optimization: Analyze system performance and identify opportunities for improvement, adapting control strategies as needed. Bridge AI Autonomous services tools with AI solver infrastructure and ontological learning models.
Documentation: Create documentation, and refine, extend, and maintain existing documentation that articulates our vision for a module, its existing implementation status, and planned stages of effort