# Internship - Machine Learning Research Engineer at Perplexity

> Hybrid · Germany · Intern · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Aperplexity%3Ab9e1ff15-d52a-46d5-abf0-26460f2a116c (read 2026-11-06).

## Facts (observed)

- Company: [Perplexity](https://alderhq.app/companies/perplexity)
- Location text: Berlin
- Where it can be done from (inferred): Hybrid · Germany (evidence: "Location: Berlin")
- Posted: 2026-09-02 (65 days ago)
- First seen by alder: 2026-11-03 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/perplexity/b9e1ff15-d52a-46d5-abf0-26460f2a116c
- Skills listed (inferred): PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Germany, n 7): p25 €77k, median €108k, p75 €155k

## Description (the employer's text)

### 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).

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aperplexity%3Ab9e1ff15-d52a-46d5-abf0-26460f2a116c and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.