Bayesian Software Engineering
Develops modular probabilistic models and algorithms for Bayesian statistical inference and machine learning in Julia, focusing on backend implementation, scaling statistical procedures, and integrating into large software systems for finance and research applications.
About the job
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
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
Core Language
- Julia (only language used in the core of our system)
Helpful Languages
- Rust, OCaml, Clojure
- C++, Haskell
Skills
Julia, Bayesian Inference, Mcmc, Smc, Vi, Probabilistic Programming, Rust, Ocaml, Clojure, C++, Haskell, Statistical Modeling, Constrained Optimization, Functional Programming, Typed Programming
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