Staff Engineer, Search

Location: US - NYC, Boston

Salary: $200,000 - $280,000

💎 About Nivoda

Nivoda is the operating system for the global jewellery industry. We connect diamond and gemstone suppliers directly with jewellery retailers across more than 70 countries, replacing a slow, offline, relationship-gated way of trading with a single digital marketplace. We're already powering over $300M in annual transactions, inside an industry worth more than $350B globally — and we're still early. Jewellery is one of the last major global industries to go digital, and we're the ones building the rails it will run on.

🤔 Our Mission

Diamond and gemstone trading has run for decades on fragmented networks, manual processes, and who-you-know relationships — which locks smaller and newer businesses out, and leaves even the biggest players losing time and margin to inefficiency. We started Nivoda to fix that: to give any jewellery business, wherever it is and whatever size it is, the same access, pricing transparency, and ease of trading as an industry giant. Our mission is to make buying and selling diamonds and gemstones radically simpler, everywhere in the world.

⭐️ The Discovery Pod

You'd be joining the Discovery Pod 🔍 — the team that decides how hundreds of thousands of diamonds and gemstones actually get found. That means ranking, recommendations, and search input: from the core search and filtering experience, to Contextual Search and a planned conversational assistant, to image-upload search that lets a buyer find a match from a photo. The same ranking and recommendation engine you'll build has to serve both Nivoda's own marketplace and the many individually branded storefronts jewellers run through our SAAS products. We're a small, high-ownership team and this pod is building the AI-powered product itself, not just using AI tools to go faster.

About the Role

We're hiring a Staff Engineer, Search to own and evolve the search infrastructure and ranking systems that power discovery across Nivoda's global marketplace.

This is a hands-on engineering role focused on building high-quality search experiences at scale — from search infrastructure and relevance tuning to ranking, retrieval and experimentation.

You'll own how products are indexed, retrieved, ranked and surfaced to buyers, ensuring search results are relevant, performant and configurable across Nivoda's marketplace and its network of jeweller storefronts.

You'll partner with engineering and product teams as search capabilities expand across the business. This is an opportunity to build the search domain from the ground up, including new ranking and embedding-based retrieval capabilities, with significant technical ownership and influence over the direction of discovery.

Tech Stack

• Search: OpenSearch, fed via CDC from PostgreSQL plus Spark/EMR/Glue ingestion jobs. No vector database anywhere in production — embedding-based retrieval, the ranking model, and image-embedding search are genuinely new builds for whoever takes the Discovery role.

• Core DB / warehouse: PostgreSQL (OLTP) → Snowflake (dbt-modeled, medallion raw/curated/presentation layers)

• Backend: TypeScript/Node.js, GraphQL API gateway, Nx monorepo + PNPM

• Auth: Keycloak · SSO: Okta

• CI/CD: Jenkins (“Nivoda Build”) + GitOps for deploys

• Cloud: AWS (EMR, Glue, DMS, Kubernetes-based deploys)

• Observability: Grafana for logs/traces

What would you be doing?

• Search Infrastructure & Architecture
• Own the design, development and operation of Nivoda's search infrastructure using OpenSearch and related technologies.

• Build and optimise indexing, retrieval and query-processing systems to support search across hundreds of thousands of diamonds and gemstones.

• Design scalable, multi-tenant search architecture that supports Nivoda's marketplace and individually branded jeweller storefronts without duplicating or forking the codebase.

• Lead the development of new embedding-based retrieval capabilities, with the opportunity to shape the underlying architecture from the ground up.

• Ranking, Relevance & Experimentation
• Own search relevance and ranking, continuously improving how products are retrieved and presented to buyers.

• Design, implement and optimise ranking strategies using signals such as product attributes, buyer intent and marketplace behaviour.

• Build evaluation frameworks to measure search quality using offline ranking metrics and experimentation techniques.

• Run relevance tuning and ranking experiments to understand whether changes meaningfully improve search outcomes.

• Establish monitoring and feedback loops to identify and address search quality and performance issues.

• Applied AI & Search Evolution
• Partner with the CTO and product teams to evolve search capabilities, including contextual and conversational search.

• Explore embedding-based retrieval, LLM-powered search and image-based discovery as extensions of the core search platform.

• Evaluate AI-powered approaches critically, building appropriate testing and evaluation frameworks rather than relying on model outputs by default.

• Design systems that handle ambiguity, zero-result queries and low-confidence results gracefully.

• Use AI coding agents to accelerate engineering while maintaining strong testing, evaluation and production reliability standards.

What do we need from you?

Years of experience: Approximately 7–10 years, with demonstrated Staff-level technical ownership and impact.

Must-have experience

• Strong experience building and operating production search systems using Elasticsearch, OpenSearch, Vespa or similar information retrieval technologies.

• Deep understanding of search relevance, ranking, query processing, indexing and retrieval optimisation.

• Hands-on experience designing and improving ranking systems, with an understanding of techniques such as learning-to-rank, relevance tuning and retrieval optimisation.

• Strong evaluation judgement — the ability to measure search quality, interpret ranking performance and determine whether a change genuinely improves relevance.

• Experience designing scalable, low-latency search systems that perform reliably under production workloads.

• Ability to own a technical domain end to end, from architecture and implementation through deployment, monitoring and continuous improvement.

• Strong engineering fundamentals and the ability to make pragmatic technical decisions in complex, evolving systems.

• Experience working cross-functionally with Product and Engineering teams, influencing technical direction and communicating complex engineering decisions clearly.

Staff-level expectations

• Demonstrated ownership of complex engineering systems, with the ability to drive technical direction independently.

• Ability to identify architectural opportunities, establish engineering standards and influence how other teams build on shared systems.

• Comfortable operating in an environment where the underlying architecture is being built and evolved, rather than simply maintaining an established platform.

Nice to have

• Experience with embedding-based retrieval, vector search technologies such as pgvector, Pinecone or Weaviate.

• Experience applying machine learning techniques to ranking and recommendations, including learning-to-rank and collaborative filtering.

• Experience building production applications using LLMs, contextual search or retrieval-augmented generation (RAG).

• Familiarity with computer vision and image embeddings, particularly image-to-attribute or image-to-product matching.

• Experience designing multi-tenant search and ranking systems for marketplaces or e-commerce platforms.

• Experience building conversational or assistant-style search experiences.

⭐️ Our culture and what we offer

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