Internship - Machine Learning Research Engineer
Perplexity · Hybrid
MeritLog read this listing from Perplexity's Ashby job board and last checked it on September 12, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- Hybrid
- Salary
- Not listed by source
- Location
- Berlin
Hiring context
How this role compares at Perplexity
Perplexity has 115 live roles in MeritLog’s catalog across 11 job families, and 21 of them are in data & analytics. 99 of those listings publish a pay range, a disclosure rate of 86%.
Perplexity concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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.
What they're asking for
- Understanding of search and retrieval systems, including quality evaluation principles and metrics.Skill
- Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.Skill
- Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.Skill
- Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).Skill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
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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