Machine Learning

This role is for one of the Weekday's clients

JobType: full-time

As a Machine Learning Engineer on the Search & Information Retrieval team, you will design, develop, and optimize machine learning solutions that enhance search relevance, ranking, and recommendations. You will work closely with product managers, data scientists, and engineers to deliver high-performance, scalable search experiences that help users discover content efficiently. This role requires expertise in ML, NLP, and information retrieval, along with hands-on experience deploying models in production.

Requirements

Key Responsibilities

• Design and implement ML models to improve search ranking, query understanding, and content retrieval.
• Develop and optimize algorithms for semantic search, personalization, and recommendation systems.
• Analyze large-scale datasets and logs to extract insights and improve search relevance.
• Build robust evaluation pipelines to measure model impact on business and user experience metrics.
• Collaborate with backend and infrastructure teams to productionize ML solutions in low-latency, scalable environments.
• Experiment with and integrate state-of-the-art NLP methods, embeddings, and large language models into search systems.
• Monitor model performance, retrain pipelines, and ensure accuracy and freshness of search results.
• Contribute to the development of search-related data pipelines, features, and tools.

What Makes You a Great Fit

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• 3+ years of experience building and deploying ML models in production environments.
• Strong understanding of search systems, information retrieval, and ranking algorithms.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
• Hands-on experience with NLP techniques, including embeddings, transformers, and text classification.
• Solid knowledge of retrieval models (BM25, vector search) and relevance evaluation metrics (NDCG, MAP).
• Experience with large-scale distributed data processing tools (Spark, Hadoop).
• Familiarity with deploying ML models via APIs or microservices.
• Bonus: experience with vector databases and semantic search frameworks (Elasticsearch, OpenSearch, FAISS), personalization systems, A/B testing, and contributions to the ML/Search community.
• Strong problem-solving skills, analytical thinking, and ability to collaborate in cross-functional teams.
• Comfortable working in Agile development environments and managing multiple priorities.