Senior 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.

The Senior AI/ML Engineer will design and operate advanced search, ranking, and recommendation systems that help customers discover the right products across a global digital marketplace. This role combines applied machine learning, information retrieval, natural language processing, and multimodal AI to transform how users search for and evaluate products. You will build production-ready solutions spanning embedding-based retrieval, learning-to-rank models, conversational search, and image-based product matching. Working within a small, high-ownership engineering team, you will lead models through the entire lifecycle, from feature development and training to deployment, evaluation, and monitoring. You will also shape technical architecture, improve search relevance, and ensure AI-powered experiences remain reliable under real-world conditions. This remote opportunity offers significant ownership, technical challenges, and the chance to build innovative AI capabilities in a rapidly growing technology environment.

Accountabilities:

The Senior AI/ML Engineer will own the development and continuous improvement of production-grade search and discovery systems, combining rigorous machine learning practices with scalable infrastructure and customer-focused product development. Key responsibilities include:

• 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.

Requirements:

The ideal candidate is an experienced AI/ML engineer with strong expertise in information retrieval, applied machine learning, and production infrastructure. They should be comfortable taking ownership of complex systems end to end, evaluating model behavior critically, and delivering reliable solutions at scale. Required qualifications include:

• 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 w…