Perplexity
Internship - Machine Learning Research Engineer
Berlin, DE
Role snapshot
Perplexity is hiring a Internship - Machine Learning Research Engineer on a internship basis. This is a remote role open to candidates in DE. The listing highlights Search.
- Company
- Perplexity
- Location
- Berlin, DE
- Work mode
- Remote
- Job type
- Internship
- Posted
- Oct 2, 2026
Who this role suits
Students and recent graduates who want hands-on experience. Internships are usually judged on curiosity, projects and coursework rather than years of work history.
How to prepare your application
- Check your résumé against the listed skills (Search) and give each one a concrete example: what you built or achieved, not just that you used it.
- Put one or two projects near the top of your résumé, with a link a recruiter can open in a few seconds.
- Revise core fundamentals for the role; early-career interviews lean heavily on basics and how you reason through a problem.
- Read what Perplexity publishes about its product or team, so you can say clearly why this role and this company.
- For remote roles, show that you communicate clearly in writing and can work without close supervision.
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About the role
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).