Bayesian Software Engineering

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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

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