We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We're hiring for the following openings:
* Engineering Program Lead
* Backend Software Engineer
* Quantitative Modelling Engineer
* Bayesian Software Engineer
* Program Synthesis Engineer
* Analytic Learning Algorithm Researcher
* Product Managers and Business Development
* Domain Experts - Valuation, Quantitative Default-Probability, or Interest Rate Prediction Models: to collaborate on use cases, from opportunity validation through production
We’ve recently released a series of recordings from one of our quarterly team retreats, to share a look into how we work, and some of the technical problems we’re solving. A few team members sat down and talked through these topics, unscripted, and real. If you consider applying to join us, they offer a glimpse into our culture, and what working with us is really like. Watch the series on YouTube » https://www.youtube.com/@plantingspace9225/videos
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We're hiring across the following areas:
* Software Engineering: Program Lead, Product - Quantitative Modeling, Bayesian, Program Synthesis
* Research: Analytic Learning Algorithms
* Domain Experts - Valuation, Quantitative Default-Probability, or Interest Rate Prediction Models: to collaborate on use cases, from opportunity validation through production
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We're hiring across the following areas:
* Software Engineering: Program Lead, Product, Product - Quantitative Modeling, Bayesian, Program Synthesis
* Domain Experts - Valuation, Quantitative Default-Probability, or Interest Rate Prediction Models: to collaborate on use cases, from opportunity validation through production
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We’re building out our team in the following areas:
We're also looking for Domain Experts to collaborate on high-value use cases -- identifying and validating opportunities, shaping them into working solutions with our engineers, and taking them from pilot to production with real users. Practitioners with expertise in Valuation Models, Quantitative Default-Probability Models, or Interest Rate Prediction Models are particularly relevant right now.
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We’re building out our team in the following areas:
We're also looking for Domain Experts to collaborate on high-value use cases -- identifying and validating opportunities, shaping them into working solutions with our engineers, and taking them from pilot to production with real users. Practitioners with expertise in Valuation Models, Quantitative Default-Probability Models, or Interest Rate Prediction Models are particularly relevant right now.
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
This is an opportunity for builders and practitioners to help shape a deep-tech product from its earliest stage.
We're also looking for Domain Experts to collaborate on high-value use cases -- identifying and validating opportunities, shaping them into working solutions with our engineers, and taking them from pilot to production with real users. Practitioners with expertise in Valuation Models, Quantitative Default-Probability Models, or Interest Rate Prediction Models are particularly relevant right now.
We’re building an AI system for analysts and scientists, based on a fundamentally new approach to reasoning and knowledge representation. Our approach differs from LLMs in that we compose algorithms symbolically to represent complex knowledge, and perform probabilistic computations. This enables the AI-driven application of statistical models to different problems, while providing the user with a verifiable reasoning path, and an assessment of the uncertainty in each answer. We are developing applications for analysis and research in domains such as Finance, Strategy Consulting, Engineering, and more.
This is an opportunity for builders and practitioners to help shape a deep-tech product from its earliest stage.
We're also looking for Domain Experts to collaborate on high-value use cases -- identifying and validating opportunities, shaping them into working solutions with our engineers, and taking them from pilot to production with real users. Practitioners with expertise in Valuation Models, Quantitative Default-Probability Models, or Interest Rate Prediction Models are particularly relevant right now.
We’re building an AI system for analysts and scientists, based on a fundamentally new approach to reasoning and knowledge representation. Our approach differs from LLMs in that we compose algorithms symbolically to represent complex knowledge, and perform probabilistic computations. This enables the AI-driven application of statistical models to different problems, while providing the user with a verifiable reasoning path, and an assessment of the uncertainty in each answer. We are developing applications for analysis and research in domains such as Finance, Strategy Consulting, Engineering, and more.
This is a chance to shape a new approach to knowledge representation from the ground up, working alongside a collaborative team driven to solve hard problems.
We’re building an AI system for analysts and scientists, based on a fundamentally new approach to reasoning and knowledge representation. Our approach differs from LLMs in that we compose algorithms symbolically to represent complex knowledge, and perform probabilistic computations. This enables the AI-driven application of statistical models to different problems, while providing the user with a verifiable reasoning path, and an assessment of the uncertainty in each answer. We are developing applications for analysis and research in domains such as Finance, Strategy Consulting, Engineering, Material Sciences, and more.
This is an opportunity for senior engineers, product managers and operational leaders to join us, and contribute to shaping a deep-tech product from its earliest stage.
We’re building an AI system for analysts and scientists, based on a fundamentally new approach to reasoning and knowledge representation. Our approach differs from LLMs in that we compose algorithms symbolically to represent complex knowledge, and perform probabilistic computations. This enables the AI-driven application of statistical models to different problems, while providing the user with a verifiable reasoning path, and an assessment of the uncertainty in each answer. We are developing applications for analysis and research in domains such as Finance, Strategy Consulting, Engineering, Material Sciences, and more.
This is an opportunity for senior engineers and product managers to join us, and contribute to shaping a deep-tech product from its earliest stage.
We're building a system that represents domain knowledge as modular probabilistic models — making analysis rigorous and transparent. Users can connect these models flexibly into larger structures. The system enforces consistency across them, and propagates uncertainty through each step. Our first applications are in finance and scientific research, with use cases ranging from equity valuation and distress monitoring, to particle physics.
We're hiring for the following openings:
* Engineering Program Lead
* Backend Software Engineer
* Quantitative Modelling Engineer
* Bayesian Software Engineer
* Program Synthesis Engineer
* Analytic Learning Algorithm Researcher
* Product Managers and Business Development
* Domain Experts - Valuation, Quantitative Default-Probability, or Interest Rate Prediction Models: to collaborate on use cases, from opportunity validation through production
We’ve recently released a series of recordings from one of our quarterly team retreats, to share a look into how we work, and some of the technical problems we’re solving. A few team members sat down and talked through these topics, unscripted, and real. If you consider applying to join us, they offer a glimpse into our culture, and what working with us is really like. Watch the series on YouTube » https://www.youtube.com/@plantingspace9225/videos
Find out more, and apply at: https://planting.space/joinus/