Role at a glance
- Salary
- Not Disclosed
- Location
- Berlin
- Work arrangement
- Hybrid
- Employment
- Full-time
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Role Summary
This is a full-time, in-person Machine Learning Research Engineer internship in Berlin, lasting 12–24 weeks. The role focuses on improving search quality through machine learning research and engineering.
What You'll Do
- Train and optimize large-scale deep learning retrieval and ranking models using PyTorch, distributed training techniques, and hardware...
- Conduct representation learning research, including contrastive learning, multilingual and multimodal modeling, and evaluation for...
- Build and optimize RAG pipelines for grounding and answer generation.
- Improve search quality through models, data, tools, or other available approaches.
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Understanding of search and retrieval systems, including quality evaluation principles and metrics; strong proficiency with PyTorch, distributed training techniques, and performance optimization for large models; interest in representation learning, including contrastive learning, dense and sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization, and robust evaluation; publication record in AI/ML conferences or workshops.
Original job description
Content provided by the employer
Original job description
Content provided by the employer
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.
Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., 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.
Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).
About the company
Perplexity
Startup
Perplexity is an innovative technology company that specializes in developing advanced artificial intelligence solutions aimed at enhancing human-computer interaction. With a focus on natural language processing and machine learning, Perplexity empowers users to access information and insights more intuitively and efficiently. The company is dedicated to creating tools that simplify complex data and foster informed decision-making, thereby transforming the way individuals and organizations engage with knowledge. Through its commitment to excellence and user-centric design, Perplexity is shaping the future of information retrieval and analysis.