必須スキル
Python
Go
PyTorch
TensorFlow
Machine Learning
LIFE AT UIPATH:
The people at Ui Path 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 Ui Path, and about our larger purpose.
Could that be you?
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 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.
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Read papers, propose model improvements, implement and test the new models.
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Think about the best human-computer interfaces to make machine learning models accessible to everyone.
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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:
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Knowledge of Python and modern ML libraries like Py Torch, Tensorflow, or JAX.
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Ability to implement models from academic papers like ICML, ICLR, NeurIPS, etc.
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Strong quantitative intuition and mastery of linear algebra, probability and statistics.
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Solid knowledge of basic data structures (list, vector, stack, heap), basic algorithms (sort, search, etc.) and associated time/memory asymptotic complexity.
Maybe you don’t tick all the boxes above—but still think you’d be great for the job? Go ahead, apply anyway. Please. 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 https://www.uipath.com/legal/trust-and-security/privacy-policy.
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UiPathについて

UiPath
PublicUiPath Inc. is a global software company that develops artificial intelligence (AI) and agentic automation and orchestration software. The company's software enables the building and orchestration of AI agents to automate complex processes and workflows.
5,001-10,000
従業員数
New York
本社所在地
$13B
企業価値
レビュー
10件のレビュー
3.8
10件のレビュー
ワークライフバランス
3.2
報酬
4.0
企業文化
4.1
キャリア
3.5
経営陣
2.8
72%
知人への推奨率
良い点
Supportive team and colleagues
Good benefits and competitive salary
Innovative projects and cutting-edge technology
改善点
Poor management and lack of direction
High pressure and overwhelming workload
Limited career advancement opportunities
給与レンジ
19件のデータ
Mid/L4
Mid/L4 · Data Engineer
1件のレポート
$82,658
年収総額
基本給
$63,660
ストック
-
ボーナス
-
$82,658
$82,658
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