AI/ML Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI/ML Engineer based in the United States.

• Build and operate search infrastructure using technologies such as Elasticsearch, OpenSearch, Vespa, and vector-search solutions to support advanced retrieval, ranking, and multimodal search.
• Design configurable, multi-tenant ranking and recommendation systems that serve a central marketplace and individually branded storefronts without duplicating codebases.
• Develop and optimize contextual search and conversational assistants using large language models (LLMs), prompt engineering, and retrieval-augmented generation techniques.
• Build image-to-attribute and image-to-product matching pipelines using image embeddings to support image-based product discovery.
• Design reliable user experiences for probabilistic AI systems, incorporating appropriate recovery paths, validation steps, and safeguards when model predictions are uncertain or incorrect.
• Define and execute search-specific evaluation strategies, including offline ranking metrics and interleaving experiments, to measure relevance and validate improvements.
• Evaluate and stress-test foundation models and LLM outputs, identifying failure modes and ensuring that model performance is assessed systematically.
• Own the full lifecycle of ranking and recommendation models, including feature engineering, training, evaluation, deployment, and ongoing monitoring.
• Apply learning-to-rank, embedding-based retrieval, and collaborative filtering techniques to improve search relevance and product recommendations.
• Design and optimize low-latency model-serving systems capable of delivering responsive search experiences under production workloads.
• Build ML infrastructure and data pipelines using AI coding agents, supported by robust testing frameworks and evaluation tools that detect retrieval and ranking defects.
• Collaborate with engineering and product stakeholders to translate customer needs into scalable, reliable AI-powered discovery capabilities.

• Approximately 5–10 years of relevant professional experience in AI/ML engineering, search infrastructure, or related technical fields.
• Hands-on experience building or operating information retrieval systems using Elasticsearch, OpenSearch, Vespa, or vector-search technologies such as pgvector, Pinecone, or Weaviate.
• End-to-end experience developing ranking and recommendation systems using learning-to-rank, embedding-based retrieval, and collaborative filtering.
• Proven ability to manage the complete machine learning lifecycle, from feature development and model training to evaluation, deployment, and monitoring.
• Production experience building applications with large language models, including prompt engineering, retrieval-augmented generation, and effective handling of zero-result queries and competing search objectives.
• Experience designing and optimizing low-latency, real-time model-serving systems for production environments.
• Strong understanding of search and ranking evaluation techniques, including offline ranking metrics and interleaving experiments, with the ability to distinguish meaningful improvements from changes that do not improve relevance.
• Practical experience working directly with foundation models and systematically evaluating, testing, and stress-testing their outputs.
• Ability to design robust systems that account for uncertainty and failure in probabilistic models, particularly when incorrect predictions could affect important user decisions.
• Familiarity with AI coding agents and the ability to use them effectively when building ML infrastructure and pipelines while maintaining rigorous testing and evaluation standards.

• Knowledge of computer vision fundamentals, particularly image embeddings and image-to-attribute or image-to-product matching pipelines.
• Experience designing configurable, multi-tenant systems that support different customer requirements without maintaining separate codebases.
• Previous experience developing conversational search products or AI-powered assistants.
• Background in search, ranking, or recommendation systems.