Search Machine Learning Research Engineer (Berlin)

Pantera Capital; Berlin; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading technology firm in Berlin is seeking an experienced Senior Machine Learning Engineer to develop next-generation search technologies, focusing on retrieval and ranking. The ideal candidate will have a deep understanding of search systems and a proven track record in AI/ML, particularly with PyTorch. Responsibilities include optimizing large-scale models, conducting research, and collaborating with multiple teams for high-quality delivery.

Qualifikationen

• Deep understanding of search and retrieval systems, including quality evaluation principles and metrics.
• Proven track record with large-scale search or recommender systems.
• Strong proficiency with PyTorch, including experience in distributed training techniques.

Aufgaben

• Push search quality forward through models and data.
• Architect and build core components of the search platform.
• Design, train, and optimize large-scale deep learning models.

Kenntnisse

Search and retrieval systems understanding

Large-scale search or recommender systems experience

Proficiency with PyTorch

Representation learning expertise

Strong publication record in AI/ML conferences

Self-driven and ownership

Experience in search or recommender systems (3+ years)

Tools

PyTorch

DeepSpeed

Jobbeschreibung

Location

Berlin

Employment Type

Full time

Department

Search

Perplexity is seeking an experienced Senior Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking.

Responsibilities

• Relentlessly push search quality forward — through models, data, tools, or any other leverage available
• Architect and build core components of the search platform and model stack
• Design, 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 advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval
• Deploy models — from boosting algorithms to LLMs — in a scalable and performant way
• Build and optimize RAG pipelines for grounding and answer generation
• Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery

Qualifications

• Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
• Proven track record with large-scale search or recommender systems
• Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models
• Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications
• Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR)
• Self-driven, with a strong sense of ownership and execution
• Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas