Senior AI​/ML Engineer

Location: US - NYC, Boston

Salary: $200,000 - $280,000

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.

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.

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.

We're hiring a Senior AI/ML Engineer for the Discovery Pod to help build and run the ranking, recommendation, and search systems buyers rely on to find the right stone or piece in seconds. This is real information retrieval and applied ML work on production infrastructure — search systems, embedding-based retrieval, learning-to-rank, LLM-powered contextual and conversational search, image embeddings for photo-based search — not configuring a hosted search widget or calling a model API and moving on.

You'll own models end to end, from features and training through evaluation, deployment, and monitoring, and you'll be expected to understand how these probabilistic systems behave and fail, because that understanding is the job, not an adjacent skill.

• Search:
  Open Search, 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:
  Type Script/Node.js, GraphQL API gateway, Nx monorepo + PNPM
• Auth:
  Keycloak
• SSO:
  Okta
• CI/CD:
  Jenkins (“Nivoda Build”) + Git Ops for deploys
• Cloud: AWS (EMR, Glue, DMS, Kubernetes-based deploys)
• Observability:
  Grafana for logs/traces

• Build and operate real search infrastructure — Elasticsearch/Open Search, Vespa, or vector-search databases like pgvector, Pinecone, or Weaviate — to power ranking and multi-modal search across Nivoda’s marketplace
• Design multi-tenant, configurable ranking and recommendation systems that serve both Nivoda’s own marketplace and many individually branded jeweller storefronts through Feeds, without forking the codebase

• Build and improve Contextual Search and our planned…