Internship - Machine Learning Research Engineer
At a glance
Junior AI Research role at Perplexity. Berlin · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Improve search quality by training and tuning large deep-learning models for retrieval and ranking
- Use PyTorch, distributed training methods and accelerated hardware to run large-model training efficiently
- Study representation learning for search, including multilingual, multimodal and contrastive approaches
- Develop and refine retrieval-augmented generation pipelines for grounded answers
What you bring
- You understand search and retrieval systems, including how to assess quality and interpret evaluation metrics
- You are proficient in PyTorch and have used distributed training and performance tuning with large models
- You have published AI or machine-learning work at a conference or workshop
Who this fits
This full-time internship suits someone focused on machine-learning research for search and retrieval. It runs for twelve to twenty-four weeks and requires working in person at the Berlin office.
From 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).