Senior/Staff Software Engineer, Search & Retrieval Infrastructure
Pinecone · Hybrid
MeritLog read this listing from Pinecone's Ashby job board and last checked it on September 10, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- Hybrid
- Salary
- $190K - $270K
- Location
- New York City
- Company website
- www.regie.ai
Hiring context
How this role compares at Pinecone
Pinecone has 6 live roles in MeritLog’s catalog across 2 job families, and 5 of them are in engineering. 6 of those listings publish a pay range, a disclosure rate of 100%.
Pinecone concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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
What they're asking for
- Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability.Experience
- Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas.Skill
- Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch.Skill
- RAG & Orchestration: You understand 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.Skill
- Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python.Skill
- Infrastructure: 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.Skill
- Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love.Skill
- Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket."Skill
- Experience building multi-tenant SaaS platforms.SkillPreferred
- Experience with retrieval evaluation frameworks-knowing how to actually measure "good" search results.SkillPreferred
- Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps).SkillPreferred
Parsed by MeritLog from the employer’s own posting. The full description follows below.
Job description
About Pinecone Pinecone is the trusted AI knowledge company. Its trusted AI knowledge platform-including its Database, Nexus, and Marketplace products-power accurate, fast, and cost-effective AI applications for more than 10,000 customers and 1M developers worldwide. Pinecone's mission is to make AI knowledgeable. For more information, visit pinecone.io http://pinecone.io. About the Team and Role: We are hiring a senior/staff software engineer to help design and build core components of our next-generation knowledge retrieval system built for the AI era – search and retrieval infrastructure that powers high-quality, scalable, and enterprise-grade agentic systems. You’ll build the framework that allows our customers to connect knowledge–synthesized from structured and unstructured data–to modern LLM-powered applications, leveraging the world’s best-in-class vector DB supporting semantic search and hybrid retrieval. This role is ideal for someone who loves backend system architecture, distributed systems, and applied AI infrastructure. It is a high impact role with significant ownership across architecture, performance, and system reliability. Responsibilities: - 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 What You’ll Bring to the Table: To thrive in this role, you don't need to check every single box, but you should be deeply passionate about how to turn data into knowledge. Systems Expertise - Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability. - Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas. AI & Retrieval - Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch. - RAG & Orchestration: You understand 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. Technical - Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python. - Infrastructure: 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. Ownership & Impact - Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love. - Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket." Bonus Points - Experience building multi-tenant SaaS platforms. - Experience with retrieval evaluation frameworks-knowing how to actually measure "good" search results. - Experience with query planning or agentic reasoning loops (e.g., teaching a system how to break down a complex prompt into multiple specific steps). Perks & Benefits: - 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 All qualified applicants will receive considerations for employment without regard to race, color, religion, sex, age, disability, marital status, familial status, sexual orientation, pregnancy, gender identity, gender expression, national origin, ancestry, citizenship status, veteran status, and any other legally protected status under federal, state, or local anti-discrimination laws.
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