Nivoda

Own and improve customer onboarding, verification, and activation systems across the frontend and backend, using event-driven architecture and data analysis to optimize conversion and engagement. | Requires 4–7 years of backend product engineering experience with TypeScript, Node.js, event-driven systems, strong SQL, data warehousing, and experimentation. | Location: US - NYC, Boston Compensation: $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 Growth Pod You'd be joining the Growth Pod 🚀 — the team responsible for everything that happens between a business discovering Nivoda and becoming one of our most valuable customers: sign-up, onboarding, verification, activation, and the ongoing work of keeping accounts engaged instead of letting them slip away. We're a small, high-ownership team inside an engineering org that moves fast and expects you to bring judgement, not just code. You'll work closely with Product, Data, and Risk, and you'll have a real say in how we solve problems that don't yet have a playbook. About the Role We’re hiring a Senior Engineer for the Growth Pod to own both the backend systems and the front-end experiences that turn a new sign-up into an active, paying customer — the services and screens that decide whether a business converts or falls out of the funnel. The problems worth solving here span Sign-up/Onboarding, Discovery, and Checkout: you’ll build the event-driven, always-on backend systems that track thousands of accounts through activation, slipping, winback, lapsed, and churned states in real time, and the seamless front-end moments that educate customers, represent the Nivoda brand, and move them toward a purchase. This isn’t a role where you ship a spec and move on: you’ll be expected to dig into the data yourself to work out what’s actually going wrong when conversion isn’t where it should be. You’ll also work with identity verification and risk providers and with sensitive credit and payment data, so judgement and rigor matter here as much as raw engineering skill. Tech Stack Search: OpenSearch fed via CDC from PostgreSQL plus Spark/EMR/Glue ingestion jobs. 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 — I’d flagged Datadog as a guess earlier based on the Frontier agent skill descriptions; the real Confluence docs point to Grafana instead, so I’ve dropped Datadog from both JDs. What would you be doing? Core Funnel & Lifecycle Systems Own and evolve the backend services behind onboarding, verification, activation, and the broader sign-up-to-purchase funnel, most likely in TypeScript/Node.js given our stack Design and build event-driven, workflow-orchestrated systems that evaluate account state continuously — activation, slipping, winback, lapsed, churned — and trigger the right action automatically, rather than relying on batch scripts Identity, Risk & Trust Integrate with third-party identity verification, KYB/KYC, and fraud/risk providers, and make sure those integrations are resilient, well-monitored, and fail safely Handle credit and payment data responsibly as part of personalization and reactivation logic, with full awareness of what’s at stake if that logic is subtly wrong Experimentation & Analysis Design and run experiments properly — including defining guardrail metrics and knowing when a result is real versus noise — for things like referral incentives, reactivation offers, and onboarding changes Write your own SQL and work directly in the warehouse (Snowflake or similar) to run cohort analysis, diagnose time-to-activation and time-to-churn, and get to the bottom of funnel problems without waiting on a data team Take on problems that don’t have an established playbook — like a credit utilization gap or a signup-to-purchase conversion decline — and help define the problem, not just solve a given brief Building with AI Use AI agents to accelerate building and testing funnel and lifecycle services, with extra care reviewing anything that touches credit, payment, or verification logic Use AI to help draft and sanity-check your own analytical queries, while owning full responsibility for verifying results before they inform a real decision Evaluate AI-generated output — personalized messaging, reactivation copy, churn-cause suggestions — critically, rather than shipping whatever the model produces uncritically What do we need from you? Years of Experience: 4 - 7 years Strong backend product engineering experience, most likely in TypeScript/Node.js, with real ownership of funnel-critical, production services Hands-on experience with event-driven architectures and workflow orchestration — workflow engines or event pipelines that evaluate state continuously and trigger actions automatically Genuine experimentation rigor: you've designed experiments properly, you understand guardrail metrics, and you know when a result is real versus noise Strong SQL and warehouse fluency (Snowflake or similar), and the confidence to do your own cohort and funnel analysis rather than waiting on a data team Comfort using AI agents to build and test software and to draft and sanity-check analytical work, paired with the judgement to catch the subtle errors a reasonable-looking AI output can hide High tolerance for ambiguity — you're energised by problems that don't have an established playbook, not just ones with a clear spec Practical fluency evaluating AI-generated output for fitness before it ships, whether that's copy, messaging, or a suggested root cause Nice to have Direct experience integrating with identity verification, KYB/KYC, or fraud and risk providers Fintech-adjacent experience handling credit, payment, or other sensitive financial data responsibly Experience in a high-growth marketplace or fintech environment with similar account lifecycle complexity Familiarity with workflow/orchestration tooling such as Temporal, AWS Step Functions, or event-streaming platforms like Kafka ⭐️ 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.

Build and operate search, ranking, and recommendation infrastructure and develop conversational search and image-matching features for a global jewellery marketplace. | Requires 5–10 years of applied machine learning and information retrieval experience, including production LLMs, low-latency serving, and evaluation of probabilistic models. | 💎 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 — I’d flagged Datadog as a guess earlier based on the Frontier agent skill descriptions; the real Confluence docs point to Grafana instead, so I’ve dropped Datadog from both JDs. 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.

Design and implement full-stack growth features using AI-assisted workflows to drive revenue and retention in a hybrid team environment. | 5+ years full-stack experience with TypeScript/Node.js and React, strong bias for action, AI tooling fluency, and ability to work hybrid in Boston. | 🏢 About Nivoda Nivoda is a fast-growing B2B marketplace transforming the global diamond and gemstone industry. We connect thousands of suppliers and retailers across 100+ countries, processing $300M+ in annual transactions - and we're just getting started. The gemstone industry is a $350 billion opportunity, and we're building the infrastructure to unlock it. We're at an inflection point - scaling fast, hiring deliberately, and rethinking how we build. We're not a company that moves slowly and ships carefully. We ship, learn, and iterate - and we're looking for engineers who love that rhythm. 🎯 Our Mission The traditional diamond supply chain is opaque, slow, and inaccessible to anyone without deep industry connections. We're changing that. Nivoda's mission is to make the global gemstone market accessible to every business, regardless of size or location. We democratise access to inventory, pricing, and logistics so that a jeweller in Boston has the same power as a trading house in Antwerp. Engineering is how we make that happen. The code you write will be used by tens of thousands of buyers and suppliers every day. ⭐️ The Growth Team You'll be joining the Growth pod - a high-priority, high-visibility squad that owns the levers that compound: acquisition, conversion, retention, and monetisation across the entire customer lifecycle. This is a team where engineering decisions show up in the growth dashboard the next morning. The pod operates like a startup inside Nivoda. You'll work closely with the PM and designer, move fast, run experiments, and have a real say in how things get built. We're hybrid in Boston - you'll be in with the team regularly, and we think the best ideas happen in the room. This pod is also where Nivoda is deliberately changing how we build. We're investing in AI-assisted engineering workflows, giving engineers real latitude to try new tools, and building a culture where experimentation is the default - not the exception. If you want to be part of shaping that, this is the team to join. 🤝 About the Role As a Senior Full-Stack Engineer on the Growth pod, you'll own end-to-end implementation of features that directly move revenue, conversion, and retention metrics. You'll work across the stack - TypeScript and Node.js on the backend, React on the frontend - and be expected to go wherever the problem takes you. This isn't a role where you pick up tickets and ship them. You'll be involved in shaping what gets built and how, contributing to technical decisions, and helping define what good engineering looks like in a team that's moving fast. You'll also be one of the people leading the way on how Nivoda uses AI in its engineering workflow - not just as a productivity tool, but as a genuine lever for building better software faster. 🚀 What would you be doing? • Build and ship features across the full stack that directly grow revenue, conversion, and retention - you'll see your work in the metrics within days of shipping. • Design and implement the technical architecture for growth features, in-app experiments, and lifecycle and monetisation flows - making pragmatic decisions that balance speed with maintainability. • Identify and build growth loops - referral mechanics, pricing and packaging experiments, and features that compound usage across both sides of the marketplace. • Use AI coding tools (Cursor, Claude, Copilot, or whatever works best for you) as a first-class part of your engineering workflow - we actively encourage this and want to learn from how you do it. • Contribute to how the team uses AI beyond your own workflow - sharing patterns, tools, and approaches that make the whole squad faster and better. • Work closely with the PM and designer in tight, fast feedback loops - you're not waiting for specs to be 'done' before you start thinking about implementation. • Run A/B experiments end-to-end: implement the test, instrument the events, analyse the results, and feed the findings back into the next iteration. • Write clean, well-tested code that the team can build on - and raise concerns when you see technical debt accumulating that will slow us down later. • Participate in architecture and technical design conversations - your opinion on how to build things matters here, not just that you can build them. • Stay close to the latest in AI tooling, engineering practices, and developer productivity - and bring what you learn back to the team. • Mentor more junior engineers and help build an engineering culture that's proud of both its speed and its craft. 🔍 What do we need from you? • 5+ years of full-stack engineering experience, with a track record of shipping production features in TypeScript/Node.js and React (or equivalent modern stack). • A genuine bias for action - you default to building and learning, not deliberating and waiting. You know how to ship something imperfect and iterate fast. • Active, fluent use of AI coding tools in your day-to-day workflow - you've moved beyond the basics and have a clear view on where AI makes you faster and where it doesn't. • Strong instincts for technical trade-offs in a fast-moving environment - you can tell the difference between debt that's fine to carry for now and debt that will hurt the team in two sprints. • Experience building and instrumenting A/B experiments, feature flags, or other data-driven engineering patterns. • Comfort working close to the product - reading dashboards, questioning metrics, and understanding the 'why' behind what you're building. • Clear, direct communication - you can explain a technical decision to a PM in two sentences and to a fellow engineer in a whiteboard session. • Based in or willing to commute to Boston regularly for in-person collaboration. ✨ Nice to have • Experience working in a growth engineering context - conversion optimisation, retention and lifecycle tooling, referral loops, pricing and monetisation, or experimentation platforms. • Familiarity with building or integrating AI-powered features in production products (LLM APIs, RAG pipelines, embedding-based search, or similar). • Experience at a high-growth marketplace or two-sided platform - you understand the complexity of building for both sides of a network. • Contributions to open source projects, technical writing, or other public engineering work. • Exposure to modern data infrastructure and analytics tooling (Snowflake, dbt, Mixpanel, Segment, or similar). 🌟 Our culture and what we offer Nivoda is building something genuinely new - and we want engineers who want to build it with us, not just ship features into it. We care about craft, speed, and impact in equal measure. We don't have a lot of rules, but we have high expectations and high trust. On this pod in particular, you'll have real latitude. We're actively investing in new ways of working, new tools, and new approaches to engineering. If you have an idea for how to do something better - try it. We'd rather learn from a failed experiment than maintain the status quo. 🎁 Our current benefits • Competitive salary: $170,000 – $190,000 USD • Hybrid working in Boston - flexibility with meaningful in-person time • Private health insurance • Unlimited holiday allowance • Flexible compensation card (meals, transport, childcare) • Access to the latest AI tooling - we'll cover the subscriptions • Opportunities for growth, learning, and career development • The chance to join during an exponential expansion phase and directly shape how the next version of Nivoda gets built 🤝 Equal Opportunity Nivoda is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We encourage applications from people of all backgrounds, identities, and experiences - even if you don't tick every box. If this role excites you, apply. Please submit your CV in English. 💬 Not quite the right role? If this isn't the perfect fit but you're excited by Nivoda, join our Talent Pool and we'll reach out when something that suits you opens up.