Columbia University - Data Science Institute

We are looking for a research software engineer eager to work in an academic environment at the cutting edge of probabilistic programming, causal inference, program synthesis and machine learning. Role: You will play an integral part in developing systems for automatic causal and probabilistic inference. Our goal is to build systems that can reason coherently about the real world, in all of its complexity and ambiguity. These systems should allow people to (semi-automatically) build sophisticated models of the world, determine causal effects, design experiments, and construct explanations. An immediate application is in algorithmic fairness. Relevant areas: - Probabilistic programming - Causal inference - Machine learning - Program synthesis - Program analysis - Automated theorem proving You’ll work with me (http://www.zenna.org/) and the DSI community. Responsibilities: - Programming language design/implementation - Performance engineering, scaling research code - Algorithm development - Application to real-world problems Preferred Qualifications: - Strong coding ability. esp. Julia, Python, C++, ML-family - Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML - Good software engineering practices - Able to progress with high degree of autonomy, and under uncertainty To apply, please write to zt2297@columbia.edu