Internship - Machine Learning Research Engineer

Perplexity · Berlinjunior

In short

▸Trabajar en mejorar la calidad de búsqueda mediante modelos de aprendizaje automático a gran escala.
▸Desarrollar e optimizar modelos de recuperación, ranking y RAG con PyTorch y técnicas de entrenamiento distribuido.
▸Destacado: oportunidad de investigación en temas avanzados como aprendizaje contrastivo y representaciones multilingües.

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What they ask for

✓Conocimiento de sistemas de búsqueda y recuperación con métricas de evaluación.
✓Dominio de PyTorch y experiencia en entrenamiento distribuido y optimización de modelos grandes.
✓Interés en aprendizaje de representaciones: aprendizaje contrastivo, vectores densos y esparcidos, fusión de representaciones.
✓Conocimiento en modelos multilingües y multimodales para búsqueda.
✓Experiencia con pipelines RAG para generación de respuestas con fundamento.
✓Publicaciones en conferencias de IA/ML como NeurIPS, ICML, ACL, EMNLP, SIGIR.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PyTorchPyTorch DistributedDeepSpeedFSDPrepresentación densarepresentación esparcidaaprendizaje contrastivofusión de representacionesalineación de representacionesevaluación robusta

Who should you write to at Perplexity?

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Internship Program Berlin Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office. Responsibilities Relentlessly push search quality forward — through models, data, tools, or any other leverage available. , PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models. Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval. Build and optimize RAG pipelines for grounding and answer generation. Qualifications Understanding of search and retrieval systems, including quality evaluation principles and metrics. Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models. Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation. , NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).

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