Senior/Staff Software Engineer, Search & Retrieval Infrastructure

🕒 il y a 3 mois

🇺🇸 États-Unis – Télétravail

💵 $190 000 - $270 000 / an

⏰ Temps Plein

🟠 Senior

🧑‍💻 Développeur Full-Stack

👻 Score fantôme 17%

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🗣️🇺🇸🇬🇧 Anglais requis

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Pinecone

51 - 200 employés

🤖 Intelligence artificielle

☁️ SaaS

🏢 Entreprise

💰 €28 000 000 Series A en 2022-03

Artificial Intelligence • SaaS • Enterprise

Pinecone est une plateforme de connaissance de premier plan qui se concentre sur le développement d'applications d'IA précises, sécurisées et évolutives grâce à sa base de données vectorielle. L'entreprise est reconnue pour alimenter les solutions d'IA des grandes entreprises, y compris Cisco, en permettant une recherche précise et sécurisée via son intégration avec Google Cloud. Pinecone propose des outils pour l'incrustation, la mise à jour et l'indexation des données via une API facile à utiliser, soutenant diverses charges de travail d'IA comme la recherche multimodale et la classification. Avec un engagement fort en matière de sécurité et de conformité, Pinecone est certifié SOC2 Type II et conforme à la norme HIPAA, ce qui en fait une solution prête pour les entreprises, entièrement gérée dans les principaux clouds tels qu'AWS, Azure et GCP.

Description

• 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

🎯 Exigences

• 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.

🏖️ Avantages

• 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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