Machine Learning Engineer, Applied AI

Role overview Develop and operate machine learning models that improve classification, search, ranking, and personalization in an AI-enabled product. The work spans model development and production delivery, with a focus on measurable quality and practical trade-offs among relevance, response time, compute cost, and reliability.

Responsibilities - Build and improve classification and ranking models, optimizing accuracy, precision, latency, and inference cost. - Improve search relevance and context retrieval with embeddings, hybrid search, and reranking. - Develop personalization using user behavior and product data. - Create datasets, evaluation pipelines, and metrics to assess quality and catch regressions. - Train, fine-tune, and deploy models through production ML pipelines. - Monitor deployed models, investigate issues, and make improvements based on evaluation and user feedback.

Requirements - At least three years of machine learning engineering experience, including production model deployment. - Strong Python skills and experience with PyTorch or JAX. - Good understanding of ML algorithms, model training, and evaluation. - Experience with classification, ranking, retrieval, recommendation, or personalization models. - Familiarity with embeddings, vector search, or information retrieval systems. - Experience building data pipelines, training workflows, and model inference services; able to assess quality through metrics and experiments. - Understanding of trade-offs among model accuracy, latency, compute cost, and reliability.

Nice to have Experience with transformer models, language-model fine-tuning or embeddings, hybrid search and reranking, retrieval evaluation, consumer recommendation systems, or large-scale ML training and inference infrastructure.