Internship - Machine Learning Research Engineer
Berlin · Berlin, Germany
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Search — team source
Department: Search
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require sponsorship for employment visa status in the United Kingdom, Germany or Serbia?
From the employer’s application form
What you’ll work on
Full postingTrain and optimize large-scale deep learning models with PyTorch
Research representation learning for search and retrieval
Build and optimize RAG pipelines for grounding and answer generation
Push search quality forward through models, data, and tools
From the employer’s posting
Train, and optimize large-scale deep learning models using frameworks like PyTorch
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
Relentlessly push search quality forward — through models, data, tools, or any other leverage available
What you’ll bring
All qualificationsCore experience
- Strong proficiency with PyTorch, including distributed training and performance optimization
- Understanding of search and retrieval systems and quality evaluation metrics
- Interest in representation learning, including contrastive and cross-lingual methods
- Publication record in AI/ML conferences or workshops
Qualification wording
Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
Understanding of search and retrieval systems, including quality evaluation principles and metrics
Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment
Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR)
Tools in this posting
- PyTorch
Source — Tool mentions in context
- 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.
- 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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 2, 2026 · 57 days ago
- Recorded sightings
- 174
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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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).
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Berlin, Germany
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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