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Nivoda
Nivoda
201 – 500 Employees
eCommerceMarketplaceRetail
Nivoda operates a diamond and gemstone marketplace serving jewelry retailers. Its platform gives retailers access to natural diamonds, lab-grown diamonds, and gemstones, with tools to add a wider selection to their in-store and online offerings. Virtual showrooms and diamond feeds help retailers present stones to customers without holding the full range in inventory. Nivoda also coordinates quality control and logistics, and offers express delivery and flexible credit terms as part of its retail solutions.
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Senior AI/ML Engineer, Remote in the United States

Develop ranking, recommendation, and multimodal search systems for Nivoda’s global jewellery marketplace and jeweller storefronts. Build production machine learning capabilities across search, product discovery, and image matching.

USUnited StatesMAMassachusettsNYNew YorkRemoteFull-timeSeniorEnglish RequiredMachine Learning Engineer
ElasticSearch

Description

  • Build and run marketplace search infrastructure for ranking and multimodal search.
  • Create configurable, multi-tenant ranking and recommendation systems for Nivoda’s marketplace and jeweller storefronts.
  • Develop contextual search and a planned conversational assistant with prompt design and retrieval-augmented methods.
  • Create image-to-attribute and image-to-product matching pipelines using image embeddings.
  • Add recovery paths and confirmation steps before probabilistic systems take high-stakes actions.
  • Define and conduct search and ranking evaluations, including offline metrics and interleaving experiments.
  • Assess and stress-test foundation model and LLM outputs.
  • Manage ranking and recommendation models through feature development, training, evaluation, deployment, and monitoring.
  • Design and optimize real-time model serving for low latency under production load.
  • Use AI coding agents to develop ML infrastructure and pipelines, supported by rigorous evaluation harnesses.
  • Join the Discovery Pod to build ranking, recommendations, search input, contextual and conversational search, and image-upload search for a global jewellery marketplace.

Requirements

  • 5–10 years of professional experience.
  • Experience with information retrieval infrastructure such as Elasticsearch/OpenSearch, Vespa, or vector databases including pgvector, Pinecone, or Weaviate.
  • Applied machine learning experience in ranking and recommendations, including learning-to-rank, embedding-based retrieval, and collaborative filtering.
  • Experience managing ML models across feature development, training, evaluation, deployment, and monitoring.
  • Production experience with NLP and LLM applications, including prompt design, retrieval-augmented methods, and handling zero-result and trade-off scenarios.
  • Experience serving models in low-latency, real-time systems under production load.
  • Ability to evaluate search and ranking systems using offline metrics and interleaving experiments.
  • Hands-on experience with foundation models and LLMs, including assessing and stress-testing their outputs.
  • Experience designing graceful failure handling for probabilistic systems.
  • Comfort using AI coding agents to build ML infrastructure and pipelines, with a strong commitment to evaluation and testing.
  • English-language CV.
  • Preferred: knowledge of computer vision fundamentals and image embeddings.
  • Preferred: experience designing configurable, multi-tenant systems.
  • Preferred: experience with conversational or assistant-style search products.
  • Preferred: experience with marketplace or e-commerce search, ranking, or recommendations.

Benefits

  • Flexible working hours.
  • Remote-first culture.
  • Opportunities for professional growth and learning.
  • Unlimited holiday allowance.
  • Opportunity to join during a period of rapid expansion.

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