Your Mission
As a research scientist, you’ll build and improve our machine learning models to solve complex problems.
UiPath is building large multi-modal models focused on taking action and moving us closer to useful general intelligence. In this role, you’ll directly contribute to our core research challenges, pushing the boundaries of model and agent capabilities.
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.
Background:
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.
What you’ll do at UiPath
- Develop novel machine learning architectures towards the goal of useful general intelligence.
- Read papers, propose model improvements, implement, and test the new models.
- Take ownership of a research agenda, including proposing and carrying out high impact research projects.
- Present research findings both internally and at machine learning conferences like ICML, ICLR, NeurIPS, etc.
- Collaborate with applied scientists to scale model training to hundreds of GPUs.
- 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.
What you’ll bring to the team
- A PhD in Machine Learning / Computer Science or equivalent practical experience in a technical field.
- Publication record at leading machine learning conferences is a significant plus (ICML, ICLR, NeurIPS, etc.).
- Proven experience in successfully driving long-term, ambiguous projects to completion, preferably with significant practical impact.
- Strong quantitative intuition and mastery of linear algebra, probability and statistics.
- Software development experience programming in Python and modern ML libraries like PyTorch, Tensorflow, or JAX.
- Excellent communication and collaboration skills, with the ability to effectively work with other researchers, as well as with infrastructure and product teams.
- Good intuition for how computers work, particularly how they interact with modern machine learning techniques.
- Experience with another programming language (e.g. Rust / C++ etc.) is a plus.
- Working knowledge of GNU/Linux.
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