Build and maintain a robust, scalable backend for a probabilistic reasoning engine, shaping implementation strategies and delivering reliable services across infrastructure and application layers.
Plantingspace jobs
A system for representing knowledge and uncertainty to support insight discovery and transparent problem-solving.
6 open roles at Plantingspace
Coordinate the engineering roadmap and quarterly planning, steer features from specification through implementation, improve technical processes, and build tools that help a collaborative team execute effectively.
Partner with engineers and industry practitioners to identify, validate, and build probabilistic modeling use cases, integrate solutions into pilot workflows, and support adoption through production.
Implement and validate production-ready probabilistic models across domains such as finance and physics, while shaping implementation approaches and improving the framework that composes them.
Experienced technologists, researchers, academics, and product professionals can submit a speculative application, highlighting relevant expertise in complex systems or bringing deep-tech products from lab to customer.
Research and develop learning algorithms for modular probabilistic models, combining mathematical analysis, algorithm proofs, and empirical experiments to advance methods with direct applications in a working system.
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