Life at UiPath:
The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power.
To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose.
Could that be you?
Overview:
Life at UiPath
The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power.To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose. Could that be you?
Overview
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 wanting to prove and better themselves in a fast-paced machine learning group. You need to be kind, courteous, motivated, self-starting, honest and responsible.

What you’ll do at UiPath:

• Build and improve modern machine learning models to solve complex ML problems.
• Read papers, propose model improvements, implement and test the new models.
• 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.

What you’ll bring to the team:

• Knowledge of Python and modern ML libraries like PyTorch, Tensorflow, or JAX.
• 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.

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Maybe you don’t tick all the boxes above— but still think you’d be great for the job? Go ahead, apply anyway. Because we know that experience comes in all shapes and sizes—and passion can’t be learned.
Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected.
We value a range of diverse backgrounds, experiences and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes. Additionally, UiPath provides reasonable accommodations for candidates on request and respects applicants’ privacy rights. To review these and other legal disclosures, visit our privacy policy

• Build and improve modern machine learning models to solve complex ML problems.
• Read papers, propose model improvements, implement and test the new models.
• 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