Senior AI/ML Engineer for Discovery Pod

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.
• 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.
• 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.
• 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.
• 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.