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Kaggle Master Jobs (NOW HIRING)

BloKchain Talent

Anti-Fraud Risk Engineer

Tempe, AZ

Master's degree + 2 years working experience in machine learning * Proficiency in at least one ... ACMICPC, NOI / IOI, Top coder, Kaggle competition winners are preferred * Research experience ...

BloKchain Talent

Anti-Fraud Risk Engineer

Tempe, AZ · On-site

Master's degree + 2 years working experience in machine learning * Proficiency in at least one ... ACMICPC, NOI / IOI, Top coder, Kaggle competition winners are preferred * Research experience ...

Aledade PBC

Staff AI Researcher

D. or Master's degree in a quantitative discipline (e.g., Computer Science[with AI/ML Major ... Winners in ACM-ICPC, NOI/IOI, Kaggle. * Working knowledge of health-tech systems, like Electronic ...

Aledade PBC

Staff AI Researcher

D. or Master's degree in a quantitative discipline (e.g., Computer Science[with AI/ML Major ... Winners in ACM-ICPC, NOI/IOI, Kaggle. * Working knowledge of health-tech systems, like Electronic ...

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How much do kaggle master jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for kaggle master in the United States is $44.35, according to ZipRecruiter salary data. Most workers in this role earn between $30.53 and $55.29 per hour, depending on experience, location, and employer.

What is a Kaggle master?

A Kaggle Master is not a traditional job title but a designation awarded to top-performing data scientists on Kaggle, a competitive machine learning platform. Companies may hire Kaggle Masters for roles like data scientist, machine learning engineer, or AI researcher due to their proven expertise in solving complex data challenges. Their strong track record in competitions demonstrates advanced skills in model development, feature engineering, and problem-solving.

What are the key skills and qualifications needed to thrive as a Kaggle master?

To thrive as a Kaggle Master, you need a deep understanding of machine learning, data analysis, and advanced programming skills, often demonstrated through a proven track record in data science competitions. Expertise in tools such as Python, R, Jupyter Notebooks, and libraries like scikit-learn, TensorFlow, or PyTorch is highly valuable, along with knowledge of version control systems like Git. Strong problem-solving ability, creativity, and the communication skills to share findings and collaborate on team competitions set top performers apart. These skills ensure that you can develop innovative, high-performing models and effectively contribute to competitive data science projects.

What are the main challenges faced by Kaggle masters in their day-to-day work?

Kaggle Masters often encounter challenges such as dealing with messy, incomplete, or unbalanced datasets and staying updated with rapidly evolving machine learning techniques. They frequently need to iterate and experiment with multiple data preprocessing, modeling, and ensemble strategies to achieve top results. Working under tight competition deadlines and effectively collaborating with team members across different time zones can also be demanding. However, overcoming these challenges is part of what makes the role highly rewarding and fosters sharp technical and problem-solving skills.

Do people get hired from Kaggle Master?

Kaggle Masters often improve their chances of being hired by demonstrating strong data science and machine learning skills through competitions. Employers value Kaggle achievements as evidence of practical expertise, but hiring also depends on overall experience, interview performance, and fit for the role.
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Kaggle Master jobs near you
Infographic showing various Kaggle Master job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $92,247 per year, or $44.3 per hour.

Applied Scientist, Data Science

Prior Labs

New York, NY • On-site

Full-time

Re-posted 20 days ago

Job description

Who we are
Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched, until now. What LLMs did for language, we're doing for tables.
We pioneered tabular foundation models: TabPFN v2 was a Nature cover story, has passed 3.5M+ downloads and 7,500+ GitHub stars, and runs in production from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi. The hardest problems - millions of rows, real-time inference, entirely new modalities - are still open, and no one else is working on them at this level.
We're a small, highly selective team of 40+ with backgrounds from Google, DeepMind, Meta, Apple, Amazon, Jane Street, and CERN, led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by Bernhard Schölkopf and Turing Award winner Yann LeCun.
In July 2026, less than 18 months after our €9M pre-seed, we joined SAP as an independent frontier AI lab - same team, mission, and open-weights models, now backed by more than €1 billion over four years.
About The Role
You'll join our data science team working with an entirely new class of AI models. As a Data Scientist at Prior Labs, you'll be the critical link between our foundation models and real-world applications - experimenting hands-on with our tabular foundation models (including TabPFN) to uncover new applications, working directly with customers to show how native tabular AI solves problems traditional methods can't, and translating what you learn back into our product roadmap.
How You'll Drive Impact:
Applied Data Science & Experimentation: Identify high-impact use cases for TFMs and build proof-of-concepts that showcase their advantages over traditional ML. Develop best-practice workflows using capabilities like in-context learning (ICL) and benchmark rigorously against existing approaches.
Customer Success: Work directly with users to understand their challenges and demonstrate TFM value through technical demos tied to real business objectives. Guide onboarding to deliver quick wins and translate user feedback into technical insights for our product team.
Community & Education: Design and deliver workshops, tutorials, and content that explains the tabular foundation model paradigm - how it differs from LLMs and traditional ML, and why it matters. Engage the data science community through Kaggle, GitHub, and public-facing work.
What We're Looking For:
  • PhD or Master's in a quantitative field, plus 3+ years of experience building and deploying ML/AI in industry, competitive ML, or open-source.
  • Deep proficiency in Python and the data science ecosystem, with hands-on experience training and deploying deep learning models in PyTorch, including modern deep learning - architectures (especially transformers)
  • Collaborative development on GitHub and strong software engineering practices
  • Ability to translate complex technical concepts into tangible value for both technical and non-technical audiences
  • Genuine curiosity about new model architectures and a drive to explore what they can do
Nice to Have:
  • Kaggle Grandmaster, Master, or Expert status
  • Experience in technical consulting, solutions engineering, MLOps, or developer advocacy
  • Contributions to open-source libraries or data science tooling
  • A portfolio of blog posts, talks, or projects that demonstrate strong technical communication

Life at Prior Labs
You'll work alongside researchers and builders who hold themselves to a very high bar - in the quality of their work and in how they work with each other. We move fast and still take the time to do things right.
Our teams are based in Berlin, Freiburg, and New York - when you're working on something as hard as TabPFN, being in the same room matters. But great people come from everywhere, and in exceptional cases we're open to remote, which usually means frequent travel to one of our offices. Wherever you're based, the whole company comes together regularly for offsites to build and celebrate together.
Our Commitments
The best products and teams are built by people with a wide range of perspectives and backgrounds. We welcome applications from all identities and walks of life - especially if you've ever felt discouraged by "not checking every box" - and provide equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are.
We care about how your data is handled - see our Recruiting Privacy Notice.

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