Research Scientist Intern (PhD)

Prior Labs·Berlinjunior

En corto

→Investigar y desarrollar modelos de inteligencia artificial para datos tabulares, con enfoque en escalabilidad y multimodalidad.
→Trabajar con arquitecturas de transformadores, series temporales y modelos causales en entornos de producción.
→Proyecto destacado: TabPFN v2, publicado en Nature, con más de 3.5M descargas y 7.5K estrellas en GitHub.

Fluency in English is required for collaboration with the global team.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

🎧¿Llegás a la entrevista? Llevá el copiloto. Nuestra extensión escucha la entrevista en vivo y te muestra anclas de 3-4 palabras desde tu CV y tu preparación — mirás, conectás, hablás. Gratis. Ver la extensión →

¿Qué piden?

✓Actualmente cursando o con doctorado en CS, matemáticas aplicadas, estadística o ingeniería eléctrica.
✓Experiencia profunda con PyTorch y scikit-learn.
✓Fundamentos sólidos de ingeniería y dominio avanzado de Python.
✓Publicaciones en NeurIPS, ICML o ICLR, o contribuciones significativas al código abierto.
✓Experiencia en análisis de datos tabulares o series temporales.
✓Disponibilidad para trabajo presencial en Berlín, Freiburg o Nueva York, con viajes frecuentes.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PyTorchscikit-learntransformerstabular datatime seriesforecastinganomaly detectioncausal inferenceproduction deploymentopen-source contributions

¿A quién escribirle en Prior Labs?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 the research team and work hands-on on an entirely new class of AI models. We have more open research problems than people to work on them, so you'll find challenges that match your interests and expertise. Benefits Strong mentorship and professional development opportunities Work with state-of-the-art ML architectures, substantial compute resources, and a world-class team Comprehensive benefits including healthcare, transportation, and fitness 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.

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