Senior/Staff Software Engineer, Search & Retrieval Infrastructure

🕒 Julho 1

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $190.000 - $270.000 / ano

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

👻 Score fantasma 26%

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Pinecone

51 - 200 funcionários

🤖 Inteligência Artificial

☁️ SaaS

🏢 Corporativo

💰 $28.000.000 Series A em 2022-03

Artificial Intelligence • SaaS • Enterprise

A Pinecone é uma plataforma de conhecimento líder que se concentra na construção de aplicativos de IA precisos, seguros e escaláveis ​​usando seu banco de dados vetorial. A empresa é conhecida por impulsionar soluções de IA para empresas de ponta, incluindo a Cisco, permitindo pesquisas precisas e seguras por meio de sua integração com o Google Cloud. A Pinecone oferece ferramentas para embedar, inserir e indexar dados através de uma API fácil de usar, suportando diversas cargas de trabalho de IA, como busca multimodal e classificação. Com um forte compromisso com a segurança e conformidade, a Pinecone possui certificação SOC2 Tipo II e conformidade com a HIPAA, tornando-se uma solução pronta para empresas, totalmente gerenciada em grandes clouds como AWS, Azure e GCP.

Descrição

• Design and build scalable platform components leveraging advanced retrieval via query planning, semantic and hybrid search, metadata-aware search, and LLM generation • Design and build optimized indexing pipelines for structured and unstructured data • Build backend services for semantic and hybrid retrieval, knowledge graph construction, and retrieval orchestration • Improve retrieval quality through evaluation and observability frameworks • Design APIs for internal and external user and agentic consumers • Optimize latency, throughput and cost across large-scale inference and retrieval workloads • Drive technical direction for reliability and security

🎯 Requisitos

• Proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems • Comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. • Direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. • Understanding the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance. • Expert in at least one major language like Go, Rust, C++, Java, or Python. • Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi. • Comfortable in a high-growth environment.

🏖️ Benefícios

• Comprehensive health coverage including medical, dental, vision, and mental health resources • 401(k) Plan • Equity award • Flexible time off • Paid parental leave • Annual Company Retreat • WFH Equipment Stipend

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