Responsibilities
- Continuously improve search performance using models, data, or system innovations
- Design and implement foundational elements of the search infrastructure and model architecture
- Develop, train, and scale deep learning models with frameworks such as PyTorch, using distributed training methods and hardware acceleration
- Focus on enhancing retrieval and ranking systems through advanced model techniques
- Perform research in representation learning, including contrastive, multilingual, and multimodal approaches
- Deploy a range of models, from traditional ranking algorithms to large language models, at scale
- Optimize retrieval-augmented generation (RAG) systems for accurate grounding and response generation
Compensation
Competitive salary and equity package
Work Arrangement
Flexible work environment with remote options
Team
Collaborative team working across AI, data, infrastructure, and product domains
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
Available for qualified candidates