Applied Scientist II

Your Mission

As an applied scientist, you'll work at the intersection between research and product. You'll build, optimize and deploy machine learning models as well as scale them up.

We’re looking for people who love machine learning and computer science, are interested in the theory behind the models, and are motivated to solve real world problems with techniques that are scalable, efficient, and reliable in production.

The ideal candidate is someone who has conducted and applied machine learning research of substantial practical importance. This position entails designing and executing large-scale experiments on complex infrastructure, requiring strong experimental intuition and programming skills. Beyond that, you need to be kind, courteous, motivated, self-starting, honest and responsible.

This role is with UiPath advanced machine learning research group. We focus on building large multi-modal models that can take action and move us closer to useful general intelligence. We are machine learning researchers and engineers working to solve the hard problem of getting machines to use software like humans do.

• Build and improve modern machine learning models to solve complex ML problems.
• Read papers, propose model improvements, implement and test the new models.
• Productionize the best performing models in a scalable and efficient manner.
• Collaborate with research scientists to scale model training to hundreds of GPUs.
• Think about the best human-computer interfaces to make machine learning models accessible to everyone.
• Run regular benchmarks and perform statistical analysis, draw conclusions on the impact of your research-based optimizations and present your findings to the team.
• Stay up to date with the latest research and technology and communicate your knowledge throughout the company.
• Mentor other members of the team and help hire new people.

• A Master’s degree or PhD in Machine Learning, Computer Science or a related field.
• Software development experience programming in Python and modern ML libraries like PyTorch, Tensorflow, or JAX.
• Experience solving real problems with Transformers and “modern” architectures.
• Experience with at least other programming language (e.g. Rust / C++ etc.).
• Knowledge and appreciation of statically typed languages and using type systems to catch bugs early, rather than at runtime.
• Ability to implement models from academic papers like ICML, ICLR, NeurIPS, etc.
• Strong quantitative intuition and mastery of linear algebra, probability and statistics.
• Solid knowledge of basic data structures (list, vector, stack, heap), basic algorithms (sort, search, etc.) and associated time/memory asymptotic complexity.
• Good knowledge of GNU/Linux.