Level

Pinecone

Senior/Staff Software Engineer, Search & Retrieval Infrastructure

Pinecone is hiring a Senior/Staff Software Engineer, Search & Retrieval Infrastructure for a remote role open to applicants in United States. It pays $190k-$270k a year and Level rates it ; you can apply on Pinecone.

Level's read on this job

  • On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
  • Pay on this posting starts 13% higher than the typical software engineer posting on Level.
  • This is listed as a remote role.
  • The posting mentions Python, Vector Db and LLM among its tools and skills.
  • Pinecone has 7 other open roles on Level.

AI in this role

Design and build core components of next-generation search and retrieval infrastructure for AI-powered agentic systems.

pythonvector-dbllmkubernetesaws
ragai-agentsdistributed-systemsbackend-architecturesearch-infrastructureinformation-retrieval

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.

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.

How we rate this

Senior/Staff Software Engineer, Search & Retrieval Infrastructure at Pinecone rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsDistributed SystemsBackend ArchitectureSearch InfrastructureInformation RetrievalPythonVector Db

Also common in software engineer jobs at this level

Pay

This job starts at
$190k
Typical software engineer job
$168k

This posting starts 13% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. Tell me about a project where distributed systems was part of your work. What did you do?
  4. Tell me about a project where backend architecture was part of your work. What did you do?
  5. Tell me about a project where search infrastructure was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, Distributed Systems, Backend Architecture, and Search Infrastructure. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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