Job description

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 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. 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? Core Search Engine - Build and operate real search infrastructure — Elasticsearch/OpenSearch, 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 Multi-Modal AI - Build and improve Contextual Search and our planned conversational assistant, using prompt design and retrieval-augmented approaches, with zero-result and tradeoff scenarios handled gracefully rather than met with a confident wrong answer - Build image-to-attribute and image-to-product-match pipelines using image embeddings for our image-upload search work - Build the product around the reality that probabilistic systems can be confidently wrong — designing recovery paths and confirmation steps before high-stakes actions, like generating a manufacturing quote from an interpreted image Experimentation & Quality - Define and run search- and ranking-specific evaluation — offline ranking metrics, interleaving experiments — to tell whether a ranking change is actually better, not just different - Evaluate and stress-test foundation model and LLM output directly as part of the engineering process, rather than trusting it by default Production ML - Own the full lifecycle of ranking and recommendation models — features, training, evaluation, deployment, and monitoring — using learning-to-rank, embedding-based retrieval, and collaborative filtering techniques - Design and optimize low-latency, real-time model-serving systems that keep search feeling instant under production load - Use AI coding agents to build ML infrastructure and pipelines, paired with rigorous evaluation harnesses that catch ranking and retrieval bugs a system can hide while looking like it’s working WHAT DO WE NEED FROM YOU? Years of experience: 5-10 years - Information retrieval infrastructure experience — building or operating real search systems (Elasticsearch/OpenSearch, Vespa, or vector-search databases like pgvector, Pinecone, or Weaviate) - Applied ML for ranking and recommendations, end to end — learning-to-rank, embedding-based retrieval, collaborative filtering — with comfort owning features, training, evaluation, deployment, and monitoring, not just calling a model API - NLP and LLM application experience — production experience building on top of language models, including prompt design, retrieval-augmented approaches, and handling zero-result and tradeoff scenarios gracefully - Low-latency serving experience — you've built and optimized real-time model-serving systems under production load, not just trained models in a notebook - Specialized evaluation skills for search and ranking quality, distinct from general growth experimentation — offline ranking metrics, interleaving experiments, and judging whether a ranking change is actually better versus just different - Direct, hands-on experience building with foundation models and LLMs as an engineering material — prompt design, retrieval-augmented approaches, and critically, evaluating and stress-testing model output rather than trusting it by default - Experience designing for graceful failure in probabilistic systems — knowing when a model is confidently wrong and building the product around that reality - Comfort using AI coding agents to build ML infrastructure and pipelines, paired with strong evaluation and testing discipline NICE TO HAVE - Computer vision fundamentals, specifically image embeddings — experience building image-to-attribute or image-to-product-match pipelines - Multi-tenant, configurable system design experience — building systems that behave differently per tenant without forking the codebase - Experience building conversational or assistant-style search products - Background in a marketplace or e-commerce company's search, ranking, or recommendations team ⭐️ OUR CULTURE AND WHAT WE OFFER We're a collaborative, high-growth team that believes in empowerment over hierarchy. You'll have real ownership from day one, the chance to work on problems most companies don't even know they have yet, and a culture that takes learning and growth seriously — because we're scaling fast, and we need you to grow with us. OUR CURRENT BENEFITS - 🏡 Flexible working hours and a remote-first culture - 📈 Real opportunities for growth and learning - 🌴 Unlimited holiday allowance - 🚀 The chance to join during an exponential expansion phase ❤️ EQUAL OPPORTUNITY Nivoda is an equal opportunity employer. We're committed to building a diverse and inclusive team, and we welcome applications from people of all backgrounds, regardless of race, gender, age, religion, sexual orientation, disability, or any other characteristic protected by law. Even if it seems you don't meet 100% of our musts, don't let that stop you from applying — we'd still love to hear from you. Please submit your CV in English. 👋 NOT QUITE THE RIGHT ROLE? Join the Nivoda Talent Pool and we'll reach out when something that's a better fit comes up.

Requirements

Must-haves

Location: US - NYC, Boston
Salary: $200,000 - $280,000
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?
Core Search Engine
Build and operate real search infrastructure — Elasticsearch/OpenSearch, 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
Multi-Modal AI
Build image-to-attribute and image-to-product-match pipelines using image embeddings for our image-upload search work
Experimentation & Quality
Define and run search- and ranking-specific evaluation — offline ranking metrics, interleaving experiments — to tell whether a ranking change is actually better, not just different
Evaluate and stress-test foundation model and LLM output directly as part of the engineering process, rather than trusting it by default
Production ML
Own the full lifecycle of ranking and recommendation models — features, training, evaluation, deployment, and monitoring — using learning-to-rank, embedding-based retrieval, and collaborative filtering techniques
Design and optimize low-latency, real-time model-serving systems that keep search feeling instant under production load

Nice-to-haves

Computer vision fundamentals, specifically image embeddings — experience building image-to-attribute or image-to-product-match pipelines
Multi-tenant, configurable system design experience — building systems that behave differently per tenant without forking the codebase
Experience building conversational or assistant-style search products
Background in a marketplace or e-commerce company's search, ranking, or recommendations team

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

goaimlllmapitypescriptnode.jsgraphqlci/cdawskubernetes

Platform: ashby
Effort: quick

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