Bayesian Software Engineering

Job description

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 are looking for Software Engineers with a Bayesian statistics background to contribute to development of our models and algorithms for statistical inference and machine learning. Tasks will focus on designing, implementing, and scaling statistical procedures that are applicable to a wide class of models and embedded within a large software system. USEFUL EXPERIENCE - Production backend software engineering - Design and implementation of probabilistic programming language features - Implementation of Bayesian inference methods such as MCMC, SMC or VI. - Statistical modeling of real-world scenarios - Constrained optimization algorithms - Functional or typed programming - Only language used in the core of our system: Julia - Can help if you don’t know Julia: Rust, OCaml, Clojure - Also useful: C++, Haskell RESPONSIBILITIES - Define new features or fixes, based on awareness of overall objectives and challenges - Commit to delivering defined features or fixes end-to-end - Define implementation strategies, and work with others to implement them - Leverage the expertise of other team members effectively - Write design documents for more complex problems - Write clean and performant code - Help other team members to deliver on their goals REQUIRED MINDSET We've found that our successful team members share some key characteristics, and as we've grown our team, these are the qualities we've learned to seek out. We take pride in our strong, collaborative culture, and these core attributes not only reflect our shared values, but can help you evaluate how well you might fit into our team: - A builder at heart: You’re passionate about building things, solving complex problems, and approaching challenges with an entrepreneurial spirit and humility. Your sharp sense of prioritisation gives you a laser focus on delivering results that uplift the entire team, moving us closer to our goal. - Results-driven: You thrive when taking full ownership of tasks, seeing them through from start to finish, and taking accountability for the results. You’re proactive, resourceful, and avoid over-complication - anticipating problems, even in complex, uncertain environments. - Growth mindset: You are intellectually curious, have a critical mind, and seek opportunities to stretch your abilities. You explore the state-of-the-art, you dig deep to truly understand a problem, and question assumptions. - A strong collaborator: You naturally communicate with clarity and purpose, ensuring your ideas and updates are easily understood. You work efficiently, embracing an iterative approach that allows for frequent progress and course correction. You’re open to direct feedback, adapting quickly and using it to improve both your work, and the performance of those around you. - Approaching problems and tasks like a project manager: - You can take a given goal and break it down into smaller parts. - You can solve problems systematically, by yourself as well as collaboratively with others. - You involve others in effective problem-solving sessions because you value getting to the best solution over being right. - You document processes well to ensure others can jump in to collaborate effectively. WANT TO KNOW MORE? On our website you can find more about our team and work culture, as well as example tasks that share some insight into the type of things team members are working on. - What we do: https://planting.space/ - Ways of work: https://planting.space/org/ - Team culture and example tasks: https://planting.space/joinus/ Our team works fully remotely, and mostly within the CET timezone.