Internship - Machine Learning Research Engineer
Berlin, Germany
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingBuild and optimize RAG pipelines for grounding and answer generation.
From the employer’s posting
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
What you’ll bring
All qualificationsCore experience
- 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.
Qualification wording
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.
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.
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).
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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Source & posting history
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.
- Status in our records
- Active
- First seen by us
- Sep 2, 2026
- Recorded sightings
- 135
- Last seen by us
- Oct 9, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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